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Record W4323789671 · doi:10.33137/ic.v30i.39495

How to Feed My Kids Italian

2022· article· en· W4323789671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueItalian Canadiana · 2022
Typearticle
Languageen
FieldComputer Science
TopicLinguistic Studies and Language Acquisition
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

I am scouring the Internet with my six-year-old daughter for a map of Italy to include in her Geography project.The flag, also required for the project, was easy to find.But the map has to be clear, and to me, must pay due respect to the southernmost tip-Sicily.We find it, much to my daughter's delight, and I point out where her Nonni are from.The village is not listed, but the province-Siracusa-is.I then show her where Zio Carmelo, her great-uncle, lives-"way up north in Veneto.""In a town very close to Venice," I add.Her eyes light up with recognition; I know she is picturing the opening credits of the children's program Are We There Yet? in which the gondolier forcefully declares, "Buongiorno!"Then we get to work on the meat of the assignment-listing seven things about the country, some of which have been suggested by her grade one teacher in the instruction sheet: Surrounding Bodies of Water, Population, Capital City, and Languages Spoken.We add "Monuments," "Museums and Works of Art," and "Food."The last reminds me that I

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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