Bibliographic record
Abstract
Focusing on the case of the superstore sector, this article sheds light on the sources of dedication to work and the contradictory dynamics operating in the world of work today. At precisely the moment that companies are demanding considerable employee commitment, they possess fewer and fewer resources to engender it. Superstores are emblematic of this paradox, organised as they are according to “just in time” and “zero inventory” principles, which require intense dedication from store staff amounting to total working time flexibility. The incentive model promoted in the sector is to promise employees compensation in the form of internal promotion. This managerial rhetoric, though addressed to all employees, today only affects a tiny minority. The impact on the dedication of store staff is analysed using a typology of this population that allows light to be shed on the different degrees and types of commitment to work that characterise it. What is found is that a significant proportion of shop floor staff adopts an attitude of resignation. Consequently, though the work gets done “despite everything,” this is primarily because disengagement by some is compensated for by over-commitment by others, in the latter case due to motivations other than internal promotion.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.976 | 0.983 |
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; the direct Gemma label and the distilled Codex classifier 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".