A Typological Look into Learning Cultures in Workplaces: From Malicious to Demanding
Bibliographic record
Abstract
This study aimed at identifying learning cultures in various workplaces thorough an emergent grounded theory study. To gather data, in-depth interviews were conducted on 127 employees of small to large companies to reach a vast breadth and depth of data. For the purpose of inclusiveness, a maximum variation strategy was adopted for sampling to select participants purposively from manufacturing, knowledge-based, business and service companies. The data were thematically analyzed at two levels, namely initial and secondary coding. To establish credibility, three dominant strategies were continuously used as member check, peer debriefing and external auditing. Consequently, a tripartite typology emerged to represent learning cultures in various enterprises based on three criteria: management approach, peers' reaction, promotion expectancy. To sum up, in the malicious learning culture, bad working habits are learnt and shared by staff and commitment to work is gradually minimized to the lowest possible. In the deterministic learning culture, a neutral learning climate dominates the workplace as staff perceive no link between self-development and job promotion. Finally, in the demanding learning culture people may clearly view sensible links between competency development and job promotion, so they try their utmost to keep up with the latest developments in their field to avert the risk of demotion or job loss. The study suggests that if enterprises plan to achieve and keep a competitive edge, they should focus firmly on creating a demanding workplace learning culture.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".