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Record W6910102941 · doi:10.3886/icpsr34941

Social Weather Stations Survey [Philippines]: Quarter I, 2003

2014· dataset· en· W6910102941 on OpenAlexaboutno aff

Bibliographic record

VenueICPSR Data Holdings · 2014
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Government (linguistics)General Social SurveySample (material)CensusSurvey samplingPublic use

Abstract

fetched live from OpenAlex

Social Weather Surveys are SWS-initiated national surveys of the general Filipino public. Dating from 1986, initially semi-annual and quarterly since 1992, these surveys are meant to supplement, not duplicate, existing government statistics. They include both core indicators monitored regularly and items on contemporary issues. The minimum sample size is 1,200. A standard Social Weather Survey has two questionnaires, one for the household head and one for a random adult. The First Quarter 2003 Social Weather Survey was fielded over March 10 - 25, 2003 throughout the country. It used face-to-face interviews of 1,200 respondents divided into random samples of 300 each in Metro Manila, the Balance of Luzon, Visayas, and Mindanao. Adults, aged 18 years and older, were asked their views on issues such as economics, politics, crime, education, reading habits, socio-demographic characteristics, and other issues of current public interest in the Philippines. The survey also gathers information from household heads about the members of the household and household characteristics. Demographic variables include sex, age, religion, education, marital status, household composition, language uses, and occupation.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.162
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.009
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.014

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 head0.109
GPT teacher head0.361
Teacher spread0.252 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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