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Record W7067368297

The mass schooling process in Portugal: a unique pathway?

2018· other· en· W7067368297 on OpenAlexaboutno aff

Bibliographic record

VenueRepositório Científico do Instituto Politécnico de Lisboa (Instituto Politécnico de Lisboa) · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsUniversalizationPortugueseEquity (law)LegislationSocial equalityLifelong learningQuarter (Canadian coin)Compulsory education
DOInot available

Abstract

fetched live from OpenAlex

This paper aims to highlight the uniqueness of the mass schooling process in Portugal, marked by a very late start and a rapid expansion since the last quarter of XX century, reinforced by the European agenda of the last decades (Education and training 2010 , 20020). It aims, also to identify the challenges that the country faced in the field of equity in education. This work is based on the analysis of legislation associated with compulsory schooling and in national and international statistics related with the evolution of success of Portuguese students in national exams and PISA. The analysis of these results took into account several sociodemographic variables (family background, region, social support). The study shows that the universalization of basic education took place in Portugal at the end of the 20th century, but currently there is a growing convergence with reference standards set for European space of education and training (preschool rates , literacy levels, early school leaving, lifelong learning). The recent economic and financial crisis in the country has caused an inversion in the very positive tendencies verified in the country regarding education, but , in the last years the progressive tendencies were renewed and we are experiencing a quick move to the goals of Education and Training 2020 agenda and having a relevant growth of Portuguese results in PISA. Equity and school success problems still remain , with strong links with social and regional inequalities, in spite of the existence of some governmental programs to overcome these issues.

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)0.008
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0060.007
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0100.002
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0010.007

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 head0.011
GPT teacher head0.279
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

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