Another organization is possible: New directions in research on alternative enterprise
Bibliographic record
Abstract
Abstract Interest in alternative enterprises is again high, yielding a wave of popular experimentation with alternative organizational models, and new scholarship. From an organizational studies perspective, what have we learned about alternative enterprises since the last prior round of such experimentation in the 1970s, and what questions remain unanswered? Reflecting historical research legacies, scholarship often remains focused on micro‐aspects of internal organizational dynamics, but recent research at the meso scale has advanced our understanding of alternatives’ field‐level construction, and their relationship to external forces and other organizational forms. Less is known, however, at the macro scale about how or why these enterprises develop and are sustained in certain contexts, although work on this front is emerging. Meanwhile, many new alternative organizational forms/practices have not been well‐studied. Future research can remedy this oversight, while also seeking to improve our understanding of the effect of external, macro and meso‐scaled dynamics of alternative enterprises. It can also seek to better explain variations in alternatives’ institutional development and effectiveness in different sectoral contexts and domains, most notably across today’s crisis‐related fronts of climate change, housing precarity, and technological change. In so doing, it could more directly speak to a rising generation’s concerns, and better enable their effective deployment of alternatives in practice.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 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".