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Record W2472049973 · doi:10.5539/res.v8n3p170

Social Reporting in Italian Public Schools in Theory and Practice

2016· article· en· W2472049973 on OpenAlexvenueno aff
Domenico Raucci, Stefano Agostinone, Lara Tarquinio

Bibliographic record

VenueReview of European Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement, Economics, and Public Policy
Canadian institutionsnot available
Fundersnot available
KeywordsStakeholderExploratory researchPublic relationsProcess (computing)Stakeholder engagementPolitical scienceDescriptive statisticsDescriptive researchSociologyPsychologyPublic administrationSocial scienceComputer science

Abstract

fetched live from OpenAlex

Some of the challenges that schools are currently facing include the stakeholders engagement, the necessity to open schools to the local territory and the need to be accountable for activities and results. The Italian reform of the school system has required schools to overcome their self-referentiality and to make themselves more accountable to stakeholders. The social reporting process can be considered as an effective response to enable schools to become accountable, triggering fruitful stakeholder engagement processes and, at the same time, implementing a management tool. This is an exploratory research paper. It aims to describe, through a questionnaire, the awareness and dissemination degree of social reporting in Public schools located in Southern Italy given the particularities of this area. Frequency percentage and descriptive statistical methods are used to interpret findings. Results show that, although social reporting is well known in theory, it is still under-used by schools in practice.

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)0.038
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.045
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.010
Science and technology studies0.0030.017
Scholarly communication0.0100.005
Open science0.0020.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.114
GPT teacher head0.367
Teacher spread0.253 · 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 designQualitative
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

Citations2
Published2016
Admission routes1
Has abstractyes

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