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Road‐kill on the information highway: repetitive strain injury in the academy

2003· article· en· W2029959794 on OpenAlexaffvenue
Jeanne Kay Guelke

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSeriousnessAccommodationComputer terminalComputer usersPsychologyComputer securityEngineeringInternet privacyComputer sciencePolitical scienceLawMechanical engineering

Abstract

fetched live from OpenAlex

Most critical scholars who study disabilities question how specific environments effectively enable or disable persons with mental or physical impairments, and avoid locating disabilities as objective realities for affected individuals. Applying such insights to intensive use of desktop computers within office environments such as universities, affords a critique of the disabling aspects of computer keyboarding and video display terminal viewing. Computer‐related repetitive strain injury is a physical impairment whose seriousness, treatment, solutions and accommodation within the workplace are currently in dispute; in part due to lobby efforts by the IT industry and computer‐intensive employers.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.615

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0160.008
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.017
GPT teacher head0.256
Teacher spread0.239 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2003
Admission routes2
Has abstractyes

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