Small, thick, and slow: Thinking about data and research publication in the Humanities in the age of Open and FAIR
Bibliographic record
Abstract
We often speak about "Scholarly Communication" as if it is a single enterprise. This is despite our experience, which suggests that every (sub)discipline has different understandings about almost every part of the process: from the value of journals, to the function of referees, to the very point of publication. This is particularly true of the Humanities, which often seem like a complete outlier when it comes to many core aspects of modern networked research communication. They have different monopolistic presses, can be even more difficult to capture using standard bibliometric tools, and, perhaps most importantly, can have completely different understandings as to the purpose and nature of their major processes and elements. In this paper, I look particularly at the question of data and their relation to research publication in the Humanities. I argue that at least some types of traditional humanities data are quite different from those dominant in other disciplines, and that the failure to recognise this has prevented us from fully understanding the strengths of traditional approaches to research in these disciplines. I conclude by discussing an approach to the publication and citation of data in the Humanities that attempts to account for these differences and make it easier to incorporate traditional research in "big data" approaches to aggregation and reuse.
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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 | 0.155 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.036 |
| Science and technology studies | 0.018 | 0.110 |
| Scholarly communication | 0.063 | 0.147 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".