How Organizations can Develop Solidarity in the Workplace? A Case Study
Bibliographic record
Abstract
Abstract The concept of community of persons, which focuses on both persons and the whole, helps understand solidarity. The latter is based on the social nature of persons. Community of persons and solidarity seems to be able to move away from the individualist perspective or the individualism-collectivism dichotomy. Using autopraxeography in a pragmatic constructivism epistemological paradigm, this article aims to explore how organizations can develop solidarity in a workplace. The experience presented takes place in a bank. It shows that communities of persons with employees and customers are both ethical and financially efficient. These communities build a dialogue between persons and organizations. Nevertheless, it is impossible to force solidarity because it could generate derision that is contrary to the wished goal. Finally, while this model is based on solidarity, it focuses solely on internal solidarity.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".