Linking Institutional Voids with Blind Spots Through Counter-Knowledge in the Spanish National Healthcare System
Bibliographic record
Abstract
The current study suggests the presence of counter-knowledge to spread misperceptions or misunderstandings arising from the existence of institutional voids. Blind spots may be partly caused by such counter-knowledge that triggers the knowledge gaps of the actors in the face of the new information and knowledge society. Find or instance, when we talk about blind spots in the Spanish National Healthcare System (SNHS), we refer to the presence of incorrect stereotypes among the different actors, the feminisation of the profession even though the elderly population they serve continues to associate the figure of the doctor with the masculine role, the lack of awareness about the importance of data protection or cyberattacks. This study suggests that counter-knowledge is likely to result in the lack of clear vision after suffering from blind spots. Such counter-knowledge hinders people from things that most of us take for granted, which creates difficulties for engagement among multifaceted stakeholders with diverse expertise and specialities to overcome blind spots.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".