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Record W2993480342 · doi:10.17583/rimcis.2019.4600

Water Management in the Media and Research: Dissemination in Catalonia and Its Capture by Private Companies

2019· article· en· W2993480342 on OpenAlexfundno aff
Dídac Jordà-Capdevila, Lluc Canals Casals

Bibliographic record

VenueInternational and Multidisciplinary Journal of Social Sciences · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
FundersAgencia Estatal de InvestigaciónCentres de Recerca de CatalunyaCanadian Institute for Advanced Research
KeywordsNewspaperBusinessPublic relationsMass mediaPolitical scienceAdvertising

Abstract

fetched live from OpenAlex

As a vital resource for human well-being, drinking water is considered a public good worldwide. However, big companies are sometimes in charge of its management leading to an interested distribution of benefits. One of the mechanisms of power that these private companies employ to retain access to water management is the control of the dissemination of information. Thus, the objective of this study is to evaluate the influence of big companies on the dissemination of water management in Catalonia by focusing on two groups of actors: general public and experts. Accordingly, we analyse the association of big companies with mass media and research institutions. First, we scrutinise local newspapers for the period 2010-2016 to compile news about water management and companies whose activity is related to water. We found some interesting correlations between the amount and subject of news, the editorial lines and relevant facts. Second, we search scientific articles about water management written by authors from Catalan research institutions. We analyse the production, research topic and funding. In this sense, we found that technological centres are the most funded by private companies and that public funding is more related to topics related to the ecosystem functioning.Â

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.563
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.369
Teacher spread0.336 · 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 teacher head, 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
Published2019
Admission routes1
Has abstractyes

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