Bibliographic record
Abstract
Purpose The purpose of this article is to summarize the relationship between the research of Jaroslav Vanek on labor-managed firms (LMFs) and the research of Gregory K. Dow on the same topic. Design/methodology/approach The article reviews the research of Jaroslav Vanek in the 1970s and explains how this influenced the publications of Gregory K. Dow extending from the 1980s to the present. A particular focus involves Dow's book “The Labor-Managed Firm: Theoretical Foundations” published by Cambridge University Press in 2018. The methodology is to present an intellectual history in narrative form. The scope of the paper is the economic theory of the LMF. Findings The article finds that Dow's interest in LMFs was stimulated by Vanek's publications from the early 1970s. However, Dow's publications in the 1980s were motivated to a large degree by efforts to overcome the limitations of Vanek's theory of the LMF, a goal that shaped much of Dow's later research in the field. Originality/value The paper illuminates the strong intellectual influence Jaroslav Vanek exerted on the economic theory of the LMF. Readers who want information about the influences on Dow's work may also find it useful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".