Bibliographic record
Abstract
Index absenteeism and working days lost 35, 83-4, 87 Africa 27 apathy 85-6 appraising threats 47 Asia 27, 70-71 austerity 23 Australia 18 Austria 18, 39, 41 autonomy see control factors awareness 114, 124, 129, 132 Bank of America 61, 62 banking 4 banking runs 21 overleverage 13-14 temporary work contracts 61, 62 Barber, Brendan 42, 57 Belgium 18 unemployment (2001-2011) 38 youth (2009-2011) 41 biofeedback 131 borrowing see debt Brazil 19 Bulgaria 38, 41, 63 bullying and violence 36, 43-4, 46 Burke, R.J. and C.L. Cooper (2008) 60 burnout and reduced social interaction 85-6 Canada 18, 59, 84 car sales 79 challenge 47, 127, 134 change 5, 6-7, 61, 103, 127 reviewing the psychological contract 41, 128-9 see also uncertainty and insecurity China 6, 17-18, 69, 79 cognitive-behavioural approach 114, 115 commitment 127 communication and information 44-5, 107-8, 109-10, 112 competitiveness and advantage 26 global competition 52-3 workforce reduction and 66, 89 confirmative biases 30-31 construction companies 90 consumerism and on-demand culture 61, 65 control factors 42-3, 107, 123, 125-6, 127 self-awareness, self-management 124, 129, 132 trade unions' role 60 Cooper, Professor Cary L. vi (2013) 113
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.699 | 0.548 |
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".