Contradictions as the driving force of collective and subjective development group employment programmes
Bibliographic record
Abstract
This paper explores the role of collective contradictions in the development of participants in a group employment programmes. It presents an analysis, rooted in cultural-historical activity theory, of longitudinal data collected during an intervention aimed at social and professional reintegration of people living a long-term unemployment situation. Notably based on contradictions typology proposed by Y. Engeström and Sannino (2011), our analysis shows a predominance of discursive manifestations of conflicts and critical-conflicts in the transformation of the group’s activity. The contribution to the resolution of contradiction and the mobilization of scientific and systematic conceptual instruments seems to promote the processes of consciousness that allow, on the one hand, a better understanding of what shapes these actions, thoughts and emotions, and on the other hand, a greater control or mastery thereof. This inclusion of conceptual knowledge in the curricula of employment programmes plays a critical role in the empowerment of participants. The analysis of the collective activity of groups aimed at social and professional integration shows the importance of dialectical contradictions as a driving force for development, both in terms of collective and subjective activity. Findings support the benefits of the group context in helping participant’s development towards SPI.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".