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Record W7165385812 · doi:10.5281/zenodo.20754978

Using Electronic Media to Improve Efficiency and Intelligibility in Teaching and Researching the Middle Ages

2005· article· en· W7165385812 on OpenAlexaff
Daniel Paul O’Donnell

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsSession (web analytics)HTML5Point (geometry)Middle AgesHookSoftware walkthroughPrint media

Abstract

fetched live from OpenAlex

A satirical multimedia lecture delivered in the Pseudo Society—the parody session of the International Congress on Medieval Studies—at the 40th Congress, Western Michigan University (Kalamazoo), 7 May 2005. (The session is selective but is reserved for parody papers.) Behind the straight-faced title the piece is a self-running send-up of digital-humanities hype: a demonstration of a glossy “multimedia scholarly desktop” that turns into HAL 9000, locks the user out across a sequence of refusals, crashes to a parody Windows “blue screen,” and ends with the monolith and star-child of Stanley Kubrick’s 2001: A Space Odyssey. It was built as a set of interlinked HTML pages with audio on auto-advancing timers, so that it plays itself. This deposit preserves a near-final working draft, not the version actually delivered: the author’s final cut was lost when the USB key holding it was overwritten, and its closing material is not recoverable (yes, I see the irony). The archive (zipped HTML) contains the byte-for-byte authored source in originals/ together with a 2026 restoration that makes the talk playable in a current browser—CSS re-pointed from defunct URLs to local files (with three minor support stylesheets reconstructed, the originals being lost), audio migrated from the obsolete Internet-Explorer Windows Media Player control to HTML5 with the original timings, and two broken references repaired. A companion PDF gives a static, page-by-page walkthrough for citation; it cannot convey the sound or the timed auto-advance, which are the point of the piece. The accompanying README.txt documents the running order and every change.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.006
Scholarly communication0.0130.013
Open science0.0010.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.003

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 head0.101
GPT teacher head0.274
Teacher spread0.172 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreMethods

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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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