Internal and external pressures
Bibliographic record
Abstract
Purpose This study aims to investigate how an organic cotton production network learns to maintain its hybrid network and its sustainability in the face of internal and external pressures. Design/methodology/approach A qualitative case study was conducted in Justa Trama, a Brazilian‐based organic cotton production network formed by six members with different roles and organisational logics. Findings The study contributes to the literature on hybrid organisations by suggesting that in the case of networks, a compromise strategy is required at the internal level and a manipulation strategy is required at the external level. The network has to learn how to engineer a compromise among internal members and to enforce change among external institutions to maintain its sustainability. Social implications The study was performed in Brazil, a country with serious social and environmental problems. The study thus informs managers of social economy organisations on how to deal with internal and external pressures to maintain their organisation's sustainability as well as policy makers on the importance of these alternative organisations and the importance of specific legislation to stimulate this type of initiative. Originality/value The body of research on how hybrid organisations learn to deal with the mutual influence of internal organisational responses and changes in external institutions is limited. Furthermore, this mutual influence has rarely been studied in the context of networks, in which multiple members have to work together to achieve organisational and network‐level objectives as well as to respond to institutional pressures.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".