Reclaiming value from academic labor: commentary by the Editors of Human Geography
Bibliographic record
Abstract
There have long been discussions about the need for an alternative publishing model for academic research. This has been made clear by the September 2017 scandal involving Third World Quarterly. The editor’s deeply problematic decision to publish an essay arguing in favor of colonialism was likely meant as click-bate to drive clicks and citations. But we should not lose sight of the fact that this latest scandal is only one recent manifestation of a long-simmering problem that has periodically commanded significant attention in the academic literature, blogs, email lists, conference sessions, and the popular press. As a direct result, over the last decade or more, new journals have been created that specifically endeavor to offer routes around corporate/capitalist academic publishing, and several existing journals have removed themselves from this profit-driven ecosystem. In this commentary, the editorial team of the journal Human Geography weighs in on what we see as the nature of the problem, what we are doing in response, what our successes have been, and what challenges remain.
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 | 0.024 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.047 | 0.068 |
| Insufficient payload (model declined to judge) | 0.002 | 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".