Water Management in the Media and Research: Dissemination in Catalonia and Its Capture by Private Companies
Bibliographic record
Abstract
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".