MétaCan
Menu
Back to cohort
Record W3122921915

In which countries and schools do disadvantaged students succeed

2018· preprint· en· W3122921915 on OpenAlexaboutno aff
Francesco Avvisati

Bibliographic record

VenueRePEc: Research Papers in Economics · 2018
Typepreprint
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantagedQuarter (Canadian coin)Mathematics educationConstruct (python library)ChinaMeaning (existential)Reading (process)PsychologyPolitical scienceEconomic growthGeographyComputer scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

PISA 2015 data show that, on average across OECD countries, as many as three out of four students from the lowest quarter of socio-economic status reach, at best, only the baseline level of proficiency (Level 2) in reading, mathematics or science. While in Canada, Denmark, Estonia, Finland, Germany, Hong Kong (China), Ireland, Japan, Korea, the Netherlands, Norway, Singapore, Slovenia and Viet Nam, more than 30% of disadvantaged students scored at Level 3 or above in all PISA subjects in 2015, and can thus be considered “academically resilient”. Students who perform at Level 3 begin to demonstrate the ability to construct the meaning of a text and form a detailed understanding from multiple independent pieces of information when reading. They can work with proportional relationships and engage in basic interpretation and reasoning when solving mathematics problems; and they can handle unfamiliar topics in science. Such skills are the foundations for success and further learning later in life. PISA data collected over a decade (in 2006, 2009, 2012 and 2015) show that several countries have been able to increase the share of academically resilient students among those in the bottom quarter of socio-economic status.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.048
GPT teacher head0.464
Teacher spread0.416 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicEducational Assessment and PedagogyFrench-language works237,207