The context, season three episode, 31, advising, mentoring, coaching, growing
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Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
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over the course of many years, I have had the opportunity of meeting a lot of wonderful creative, passionate people. And as we would speak and I would hear about their ideas. They're probably projects, their challenges. I would be becoming their advisor, or their mentor, and I see these as different activities that complement the efforts that of course in first person, the enterpreneur the professional takes as they grow, and understand how to succeed in what they set out to achieve as an advisor, I would be very explicit that my assessment and analysis and feedback would be towards the company, even though I will be typically speaking to the founders CEO. And I would tell them, hey, this is going well this is going good, not that well, but I will always be also very explicit. I am helping you as a piece of the company you report to the board. As you are tasked to making it grow and succeed. Beware. One of the pieces of advice that I may end up getting you in the future or not. Could
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be that you need to find your replacement, because for example, you are unable or unwilling to acquire the skills needed in order to bring your project to its next necessary stage. And that is something that was always quite useful because they could correctly, assess my role as a mentor. On the other hand, I will be looking at their growth. Not necessarily, and in, you know, in a very limited fashion. Occasionally, in terms of how their personal life was going, but in terms of how the became through the years, able to address a correct self assessment. A to build their personal brand, or to understand the direction where their professional trajectory was was leading them. The coaching is something that has been reserved historically, Over the past several decades to top athletes or Hollywood stars or CEOs of large companies who pay 1000s of dollars per month or more, to be able to
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have
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a sparring partner who highlights. What are the necessary steps that they need to be making in order to maximize their opportunities, overcome their challenges and make makes sense for what, in the path that they chose for themselves. It is something that supposedly is very valuable, otherwise there wouldn't be such a market for this kind of advice. This kind of ability to build a deep and important relationship with another person, that is sufficiently independent. In order to be able to show you that their recommendations are giving you input. That is not tainted by other considerations. but at the same time has the ability of building a close enough relationship, to understand what may be blocking you from fulfilling your potential,
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it.
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I am always interested and excited about opportunities to make technologies or approaches that previously were very elitist very reserved a very exclusive available to the broadest possible number of people instead. And in working with Torah, in the turret coach initiative where I am head of Torah coach. That is what we are trying to do to make coaching available to everyone, to disrupt the business model of coaching to really democratize the access to this wonderful tool of personal and professional development. The way that this is achieved is through a different approach, where there is a very affordable monthly subscription. That is really a token to demonstrate that the person is serious about their commitment in time in taking advantage of the advice that they receive in implementing the necessary steps. But it is really affordable, making it so that anybody can take advantage of it, and where the interests of the subscriber really align with the interests of Dora coach is in
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the success of finding their new job. Because really, that is what is the objective of Torah, in general and specifically of Torah coach that aims to push people to overcome whatever self imposed limitations. They have found, whatever, experiences, especially in this past year, they have been exposed to and allow them to redefine their opportunities and their potential, and then take advantage of the Torah coach platform as well as of Torah, in general, to identify and prepare to close their job opportunity, as early as possible. And it is after they gain this new job that based on what is called an income sharing agreement, they will pay a certain percentage for a limited number of months tutorial. A, which is what makes tourism platform mentor or coach financially viable. The ability to work remotely is opening new doors to professionals worldwide, every day, and to companies that realize that their way of thinking, was an obstacle that they can completely eliminate when they
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realize that the office is over, remote is here to stay. Digital is here to stay. This is one of the important lessons that everyone needs to really acquire and and digest and make their own. It is going to be possible to grow professionally so differently, and I believe so much better. As we take advantage of all the digital tools that we have available and eliminating the barrier of physical distance will expose us to growth journeys that we wouldn't have imagined possible before. So, check it out. Check out Doray by itself, if you haven't created your professional genome yet, especially, pay attention to the psychometric
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part, which is quite interesting and intriguing, it allows you to, through a series of of questions quite deep, by the way, it will take you about half an hour to complete. Not only your experience and skills, but also your attitudes your culture. And as a consequence, highlight your compatibility with the team that constitutes your next future job. So there's a lot that you can learn just by doing that about yourself. And there are many, many other ways that creating your complete professional genome entourage can be valuable. And then of course, check out direct coach, where you will be able to have one on one sessions. In order to analyze your needs. You will be able to have sessions with peers, where you may be you will address a concern that somebody in your same situation, shares masterminds with experts that based on their experience. Teach best practices. For example, how to be the best possible remote team member that for many professionals is still new and self evaluation
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tools that show you where you are with respect of where you want to be. So, A, advising, mentoring, coaching, are all part of a wonderful set of human relationships that we can leverage, thanks to technology, in person, remotely. When algorithms and artificial intelligence, take care of so much of what we shouldn't be doing, because it can be taken over by computers. There is time that we can take advantage of. In order to build, human relationships
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The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.
In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.
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Narrative Influence Propagation:
? The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Degree Distribution:
? (based on kolmogorov-smirnov test) ? switch to linear
Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.
This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.
Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
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We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.
positive: | negative: | neutral:
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Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get better results (slower). Standard model is based on a fixed AFINN dictionary and works faster, but for English only.
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Main Topical Groups:
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+ ⤓ ? ⤓ The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
Most Influential Elements:
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+ ⤓ ↻ ?We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.
Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
Network Structure:
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?The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.
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Action Advice:
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Structural Gap
(ask a research question that would link these two topics):N/A
?A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
Latent Topical Brokers
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?These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.
These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.
These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.
Emerging Keywords
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Evolution of Topics
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The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative frequency of occurrence.
Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).
Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).
Main Topics
(according to Latent Dirichlet Allocation):loading...
⤓ ? ⤓LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.
We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
Most Influential Words
(main topics and words according to LDA):loading...
We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.
Sentiment Analysis
positive: | negative: | neutral:
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We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.
The approach is based on AFINN and Emoji Sentiment Ranking
The approach is based on AFINN and Emoji Sentiment Ranking
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get better results (slower). Standard model is based on a fixed AFINN dictionary and works faster, but for English only.
Text Statistics:
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Network Statistics:
Top Relations / Bigrams
(both directions):
The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).