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Record W7051404668

Mariposa '77

2010· book· en· W7051404668 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2010
Typebook
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsPerforming artsScheduleFolkloreStatement (logic)Minor (academic)
DOInot available

Abstract

fetched live from OpenAlex

Item consists of the 69 page Mariposa Folk Festival program for 1977, held on Toronto Islands, Toronto, Ontario, from 24-26 June, 1977. The program was edited by Bill Garrett, designed by Sharon Johnston and the advertisments were managed by Trish Short. Program includes a map of the site, a schedule of evening and day concerts and workshops and a list of performer biographies. Also contains several articles including: "Mariposa: An Introduction" by Estelle Klein, Artistic Director, "Mariposa in the Schools" by Carol Kehm, "Children's Area" by Sharon Hampson and Lois Lilienstein and "Indians and Inuit at Mariposa" also by Estelle Klein. This program also contains a supplementary newsletter called "The Newsletter: A Mariposa Folk Festival Publication Special Program Edition '77" edited by Matthew Clark and Marilyn Koop, designed by Stew Cameron and with general support from Bill Garrett, Grit Laskin and Joyce Yamamoto. The supplement contains several articles including: "Fifteen Months of Music: The Folk Instruments of South America" by Quentin and Edmond Badoux, "If You Miss Me On The Picket Line.." by Matthew Clark, "Please Walk On The Grass" by Ian Robb, "Record Reviews" with contributors: Marilyn Koop, Edith Fowke and Matthew Clark and "Grit's Workshop" by Grit Laskin. Program also contains a financial statement by the festival foundation.

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)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.708
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.001
Scholarly communication0.0070.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.7080.610

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 head0.007
GPT teacher head0.154
Teacher spread0.147 · 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
GenreOther

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
Published2010
Admission routes1
Has abstractyes

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