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Record W6962674047 · doi:10.17605/osf.io/mjfz3

Familydemic Cross Country and Gender Dataset on work and family outcomes during Covid-19 pandemic

2022· article· en· W6962674047 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueOSF Preprints (OSF Preprints) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsDownloadWork (physics)PandemicCross countryWelfareUnpaid workInternational comparisonsSurvey data collection

Abstract

fetched live from OpenAlex

Here we offer open access to the Familydemic Cross Country and Gender Dataset (FCCGD), which offers cross country and gender comparative data on work and family outcomes among parents of dependent children, before and during the Covid-19 pandemic. It covers six countries from two different continents representing diverse welfare regimes as well as policy reactions to the pandemic outbreak. The FCCGD was created using the first wave of a comparative, web-based international survey (Familydemic) carried out between June and September 2021, on representative samples of parents (aged 20-59) living with at least one child under 12 in Canada, Germany, Italy, Poland, Sweden and the US. While individual datasets are not available due to country-level restriction policies, the presented database allows for cross-country comparison of a wide range of employment outcomes and work arrangements, the division of diverse tasks of unpaid labour (housework and childcare) in couples, experiences with childcare and school closures due to pandemic and subjective assessments of changes to work-life balance, career prospects and the financial situation of families. The detailed description of how the dataset was created can be found in Data Descriptor published in Scientific Data (Springer Nature): https://rdcu.be/c2GwL IMPORTANT: Before accessing the data please DOWNLOAD IT as the built-in OSF browser distorts the tables.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0490.018

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 head0.056
GPT teacher head0.345
Teacher spread0.289 · 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