Divercity podcast: building resilience within work through behavioural science
Bibliographic record
Abstract
In this episode, host Julia Streets is joined by Professor Grace Lordan, Associate Professor in Behavioural Science at the London School of Economics and Richard Nesbitt, Adjunct Professor of the Rotman School of Management, University of Toronto and a Visiting Professor at the London School of Economics. We explore how the adoption of technology is fuelling financial services, how emerging talent must and can build resilience in the working environment, the importance of inclusion at key stages of education, building inclusion into the policy and culture of the workplace, and navigating through toxic cultures. At the launch of the “Inclusion Initiative”, Professor Lordan reports back on her academic research from many interviews and industry discussions, examining how inclusion and diversity can drive change and ultimately better results. This episode was recorded at the London School of Economics before the UK COVID-19 Lockdown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".