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Record W4281394246 · doi:10.5539/jms.v12n1p152

Managerial Analysis of the Overcrowding Schools Situation in Algeria

2022· article· en· W4281394246 on OpenAlexvenueno aff
Kadri Nabila, Souad Sassi Boudemagh

Bibliographic record

VenueJournal of Management and Sustainability · 2022
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
FundersDirection Générale de la Recherche Scientifique et du Développement Technologique
KeywordsOvercrowdingIndependence (probability theory)Quality (philosophy)Face (sociological concept)Software packageStatistical analysisProcess (computing)Public relationsMedical educationComputer scienceSociologyPolitical scienceSoftwareSocial scienceStatisticsMedicine

Abstract

fetched live from OpenAlex

To provide quality education, the education sector in Algeria has undergone several transformations and significant changes since its independence. The current paper aims to explain the current, problematic situation and analyze it through adapting a qualitative approach. The analysis is based on the process of planning and programming of public schools in Algeria, taking as a case study the public primary school of the new city, Ali Mendjeli, in Constantine. The data for this study were collected using a semistructured face-to-face questionnaire, an interview, and a discourse analysis applied to the press documents. The data gathered from the questionnaires were processed using Statistical Package for the Social Sciences (SPSS) software. The results revealed that the main factors of schools overcrowding in Algeria exist at various levels: strategic, tactical, and operational.

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)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.003
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.418
Teacher spread0.390 · 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
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

Citations1
Published2022
Admission routes1
Has abstractyes

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