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Record W3021834764 · doi:10.14658/pupj-ijse-2020-1-16

Far from the Ivory Tower? Italian Academics’ and Higher Education Institutions’ Societal Impact and Contributions to Economic Development

2020· article· en· W3021834764 on OpenAlexaboutno aff
Claudia Rocca, Stefania Maggi, Cinzia Albanesi, Esther Briner, Augusta Nicoli, L Borghi, Chiara Reali

Bibliographic record

VenueArchivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsIvory towerHigher educationPolitical scienceSociologyEconomic growthEconomicsLaw

Abstract

fetched live from OpenAlex

Cross-sector collaborations are some of the strategies used to promote early childhood development and wellbeing. Without these collaborations, key services for families with young children may be missed or even duplicated. By drawing from experiences in Canada and Italy, we share findings from a study that aimed to understand the factors that make cross-sector collaborations (CSC) succeed or fail. Specifically, the study focused on understanding how CSC promoting early child development are created, maintained, and consolidated; and on identifying the social psychological, organizational, and economic aspects of CSC that help or hinder their functioning. Based on qualitative analysis of data gathered from four focus groups and thirteen interviews conducted across seven Canadian and Italian communities, we conclude that the success of CSC depend of a series of factors that transcend context, language and culture.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.588

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.008
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.296
Teacher spread0.267 · 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.

Study designObservational
DomainEvaluation
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
Published2020
Admission routes1
Has abstractyes

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Same venueArchivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)Same topicHigher Education Governance and DevelopmentFrench-language works237,207