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Record W4238350557 · doi:10.3389/feduc.2021.765369

Corrigendum: Measuring Privileged Identity in Educational Environments: Development and Validation of the Privileged Identity Exploration Scale

2021· erratum· en· W4238350557 on OpenAlexaff
Sherry K. Watt, John A. Mueller, Eugene T. Parker, Kira Pasquesi, Cindy Ann Kilgo, Amanda L. Mollet, Duhita Mahatmya

Bibliographic record

VenueFrontiers in Education · 2021
Typeerratum
Languageen
FieldSocial Sciences
TopicJewish Identity and Society
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConceptualizationIdentity (music)Scale (ratio)MulticulturalismState (computer science)Statement (logic)Computer scienceEngineering ethicsData scienceEpistemologySociologyPolitical sciencePedagogyLawArtificial intelligenceEngineeringCartographyAlgorithmGeographyAesthetics

Abstract

fetched live from OpenAlex

Addition of an AuthorThe Multicultural Initiatives (MCI) Consortium was not included as an author in the published article. The corrected Author Contributions Statement appears below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated. The MCI Consortium includes current and alumni members of the Multicultural Initiatives Research Team who contributed to the conceptualization of the final manuscript.Text CorrectionIn the original article, there was an error. In the title, the name of the scale (Privileged Identify Exploration Scale) is incorrect. A correction has been made to the title – Measuring Privileged Identity in Educational Environments: Development and Validation of the Privileged Identity Exploration Scale. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2021
Admission routes1
Has abstractyes

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