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Record W3089043212 · doi:10.5539/ies.v13n10p124

The Extent of Practicing Management by Love in the Inclusive Schools in Qatar in Accordance with Several Variables from the Perspective of Administrators

2020· article· en· W3089043212 on OpenAlexvenueno aff
Abdulnaser Fakhrou, Ibrahim Ali Al-Baher, Sara A. Ghareeb

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
FundersQatar University
KeywordsPsychologyPerspective (graphical)Higher educationSignificant differenceMedical educationSocial psychologySociologyPolitical scienceMathematicsMedicine

Abstract

fetched live from OpenAlex

The present study aimed to explore the extent of practicing management by love in the inclusive schools in Qatar from the perspective of administrators at these schools. The sample consists from 342 administrators who were selected through using the random clustering sampling method. A descriptive approach was adopted. A questionnaire was used to meet the study’s goals. It was found that the extent of practicing management by love in the inclusive schools in Qatar is moderate. It was found that there is a statistically significant difference–at the statistical significance level of (a ≤ 0.05)–between the respondents’ attitudes which can be attributed to gender. The latter differences are for the favor of males. It was found that there isn’t any statistically significant difference –at the statistical significance level of (a ≤ 0.05)–between the respondents’ attitudes which can be attributed to experience, or academic qualification. The researchers recommend conducting more studies that aim at shedding a light on management by love and its impact on the performance of educational institutions.

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)0.002
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.044
GPT teacher head0.384
Teacher spread0.340 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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