Bibliographic record
Abstract
Abstract Absenteeism is the failure to report for scheduled work. Organizations are interested in absenteeism because of its cost, while scholars are interested because it indicates something essential about the nature of employees' attachment to the organization. There is no dominant theory of absenteeism, in part because the surface similarity of the behavior masks a wide variety of causes. However, several “models” of absenteeism can be posited to reflect this assumed causal diversity. These models implicate demographics, health, deviant tendencies, economic motives, competing demands, withdrawal from aversion, and social influence as causes or correlates of absence. The deviance model has dominated management approaches to absence. As a result, punishment and discipline systems are the most common methods of controlling absence. Used alone, they are not especially effective because of negative side effects and because few employees are actually punished since few exhibit excessive absence. More effective are multiple consequence systems that punish extreme offenders but reward good attenders. Job enrichment, flextime, and employer‐sponsored fitness programs have been shown to reduce absence, as have self‐management and feedback programs that teach employees to regulate their own attendance behavior.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".