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Record W4401950865 · doi:10.4324/9781003452027-9

All in a name

2024· book-chapter· en· W4401950865 on OpenAlexaboutno aff
Adriana Ortiz

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceHistory

Abstract

fetched live from OpenAlex

This chapter is an autoethnography of the author’s linguistic and cultural trajectory as a plurilingual speaker. Starting from the etymology of the names given to her at birth and her life experiences, she draws relationships with her cultural and linguistic development showcasing the interrelatedness between language, culture and identity. Through a brief history of her connection with the written word and language, she reflects on the deep Spanish roots of her name, her beginnings as an English speaker, her exposure to other languages, the adoption of plurilingual views as a new Canadian citizen, and the responsibility of being a heritage language mother. The results of her introspection highlight the identities that have shaped her over the years: as a student, as a writer, as an educator, as a mother of bilingual children and now as a strong proponent of plurilingual pedagogies.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.109
Threshold uncertainty score0.364

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1090.056

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 head0.163
GPT teacher head0.500
Teacher spread0.337 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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