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Record W7086777310 · doi:10.7929/issda/zzlyv4

Quarterly National Household Survey (QNHS) Module on Equality Q4 2010

2025· dataset· en· W7086777310 on OpenAlexaboutno aff

Bibliographic record

VenueIrish Social Sciences Data Archive (ISSDA) · 2025
Typedataset
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMicrodata (statistics)Quarter (Canadian coin)National accountsSurvey data collectionSurvey researchTime-use surveyGeneral Social SurveyCurrent Population Survey

Abstract

fetched live from OpenAlex

The Quarterly National Household Survey (QNHS) began in September 1997 and ran until Q2 2017 when it was replaced by the Labour Force Survey (LFS). While the main purpose of the QNHS was the production of quarterly labour force estimates, the QHNS also conducted special modules on different social topics each quarter. A module on Equality was included in the Quarterly National Household Survey (QNHS) in the three months from October to December 2010 (quarter 4). The questionnaire referred to discrimination experienced in the two years previous to that time period. An equality module was also included on the QNHS in the fourth quarter of 2004. However, it should be noted that in 2009 the QNHS moved from seasonal to calendar quarters. Therefore, the 2004 survey was conducted from September to November. Please note that due to CSO restrictions, these files cannot be matched to their associated microdata files.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.071
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0710.069

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 head0.382
GPT teacher head0.448
Teacher spread0.065 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

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