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Record W2110140608 · doi:10.1136/ip.2004.005942

Do world conferences live up to their promise?: Table 1

2004· editorial· en· W2110140608 on OpenAlexaffabout
I B Pless, Frederick P. Rivara

Bibliographic record

VenueInjury Prevention · 2004
Typeeditorial
Languageen
FieldSocial Sciences
TopicConferences and Exhibitions Management
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsDelegateAttendanceCertaintyPublishingPromotion (chess)Desert (philosophy)Public relationsPolitical sciencePsychologyHistoryLawPoliticsComputer science

Abstract

fetched live from OpenAlex

Thoughtful feedback is needed This issue of Injury Prevention is scheduled for distribution at the 7th World Conference on Injury Prevention and Safety Promotion in Vienna. Barring the predictably unpredictable quirks of publishing, each delegate will have received a copy. Many can still recall the first conference in Sweden 15 or so years ago, and some will have attended each successive meeting. The total of attendees, past and present, may now be large enough to begin to try to assess how well these biennial pilgrimages meet their goals. There is no way to judge with certainty the success of a conference. Much may depend on the weather (awful in Montreal, delightful in Melbourne) or on which of our old friends showed up. One criterion for success is that held by the organizers: a good balance sheet, which equates to the number of attendees. But bigger is not necessarily better. Balance may be more important—fewer attendees from more countries. For example, this conference may have attracted a higher-than-usual number from some European countries. Viewing the world through the undoubtedly distorted lens of an editor, my impression has been that much of Europe is a desert when it comes to injury prevention. If it proves true that many of the papers given in Vienna originated from those desert lands, this would be one positive score. Still, total attendance may be an appropriate measure even if it is confounded by location, which, in turn, reflects cost considerations. Montreal is easier to reach than Delhi and who could resist Vienna except those with shallow pocketbooks. Apart from numbers and …

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.030
GPT teacher head0.344
Teacher spread0.314 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations4
Published2004
Admission routes2
Has abstractyes

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