The Popularization of Specialized Knowledge Through Ted Talks: The Case of Positive Psychology
Bibliographic record
Abstract
TED is a non-profit global platform where conferences and speeches—brief but powerful—are held by people who, based on the TED’s motto, have an idea considered to be worth spreading. TED is often regarded as one of the best examples of positive globalization in its activity of knowledge-sharing and it defines itself as “a global community welcoming people from every discipline and culture who seek a deeper understanding of the world” (Note 1). As Heller (2012) said, TED’s talks are “sophisticated, popular, lucrative, socially conscious, and wildly pervasive—the Holy Grail of digital-age production”. However, in some recent newspaper articles TED’s approach to the dissemination of science has been criticized because considered simplistic, trivial and even biased (Bratton, 2013; Robbins, 2012). Notwithstanding, current studies in TED’s approach to scientific popularisation show that science is directly brought into contact with people, without any mediation (Scotto di Carlo, 2014a). The aim of this paper is to examine how a discipline such as positive psychology is represented in some successful speeches delivered by specialists at TED events. I will focus on the main linguistic and extra-linguistic strategies—such as non-verbal elements—used by experts and academics to convey specialized knowledge to lay people by using the main tools offered by discourse analysis. This will help to clarify whether this process of knowledge-dissemination established by this hybrid genre, is an effective mode of construing, representing and transmitting scientific information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.059 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".