Knowledge equity as social justice in academic publishing and why it matters for accounting research
Bibliographic record
Abstract
Purpose The current academic publishing model, in which researchers rely significantly on multinational publishing companies to disseminate their work, has implications for knowledge enterprise both in terms of knowledge production and distribution. This study aims to provide a critical reflection on the academic publishing model and how it works, particularly in light of the rise of open access publishing and the growing analytics focus of publishing companies and discusses the impact on knowledge equity. Design/methodology/approach This exploratory essay offers a critical analysis of the impact of the current academic publishing model on research practices. The discussion provides a foundation for the argument that knowledge equity is essential to social justice. Findings To effectively fulfil the transformative aims of the interdisciplinary research community within social and environmental accounting, it is imperative to establish equitable access to published research. Originality/value This essay opens space for discussion of the current publishing model, given its dominance of the knowledge enterprise. It outlines some of the implications of this model for knowledge equity and suggests strategies for fostering a more inclusive and accessible dissemination of scholarly work.
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 | 0.047 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.013 | 0.094 |
| Scholarly communication | 0.044 | 0.033 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".