Out of the Ivory Tower, into the Digital World? Democratising Scholarly Exchange
Bibliographic record
Abstract
Abstract The year 2020 has witnessed an unprecedented expansion of scholarly events online. Yet, in the scramble to adapt to difficult circumstances, little reflection has been given to the ways in which these new digital landscapes can reshape our approach to public history more permanently. This article draws upon the authors’ experiences as organisers of the 2020 AskHistorians Digital Conference (AHDC). As one of the first pandemic‐era conferences to be ‘born digital’, The 2020 AHDC leveraged its online format to challenge the exclusionary nature of traditional academic conferences. By reducing barriers to both participation and access, the event blended scholarly exchange with public engagement on a remarkable scale, reaching a global audience of tens of thousands. In sharing the lessons learned from this undertaking, we argue that digital conferences are not a temporary expediency; rather, they present a revolutionary opportunity not only to reshape the ways in which scholarly conversations take place, but also to reduce artificial divides between academic and public histories.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.034 |
| Scholarly communication | 0.027 | 0.033 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 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".