Bibliographic record
Abstract
Marketing departments, governments and policymakers all around the world have increasingly started embracing the field of behavioural sciences in improving the design of products and services, enhancing communications, improving managerial decision-making, encouraging desired behaviour by stakeholders and, more generally, creating a human-centric marketplace. Within organisations, the human resources management (HRM) function is perhaps the one place that acknowledges that humans are central to the organisation’s success, so it is critical that HRM too actively embraces the insights and methods of behavioural sciences. In this article, I provide an overview of the behavioural sciences, discuss how HRM can benefit from an in-depth knowledge of the science and illustrate specific examples from recruitment processes, training and communications, incentive design, employee-oriented processes, and diversity and inclusion initiatives that could benefit from evidence from behavioural sciences.
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 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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".