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Record W4210879327 · doi:10.1111/1468-229x.13259

Out of the Ivory Tower, into the Digital World? Democratising Scholarly Exchange

2022· article· en· W4210879327 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHistory · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIvory towerMedia studiesEvent (particle physics)Digital mediaPublic relationsPublic engagementPolitical scienceSociologyReflection (computer programming)Computer scienceLaw

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.505
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1150.000

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.

Opus teacher head0.028
GPT teacher head0.216
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it