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Record W7134836332 · doi:10.58066/h0sc-j206

Haiku Canada Newsletter, Vol. 09 (December 2024)

2024· article· en· W7134836332 on OpenAlexaboutno aff
Haiku Canada

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEarthquake and Disaster Impact Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHaikuTable (database)George (robot)Canadian literature

Abstract

fetched live from OpenAlex

A seasonal newsletter for Haiku Canada's British Columbia and Territories region. In this issue: interview / entrevue with Greg and Beth Skala haiku challenge / défi haïku Let's celebrate! special projects/ projets spéciaux at your calendar / à votre calendrier present moment... and beyond coming up / à l'affiche

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial · Consensus signal: none
Teacher disagreement score0.479
Threshold uncertainty score0.743

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.001
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.4790.288

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 head0.031
GPT teacher head0.295
Teacher spread0.264 · 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
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

Citations0
Published2024
Admission routes1
Has abstractyes

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