Gap in Techno-Scientific Activity: The Iberoamerican Context
Bibliographic record
Abstract
The aim of this paper is to apply the hermeneutic approach to social imaginaries of science and technology. Special attention will be given to discuss how the techno-scientific system intends to face poverty, although the system focuses to a different direction, which is a growing inter-penetration with the economic system. The techno-scientific system, as well as its politics, sets aside poor people. In such a context, the scientific-technologic gap continues to grow, generating gradually two different paces of international development. Factors like the cyborgization, robotization, the development of ITCs, etc., have an impact into the poorest regions, ending up into situations of technologic-scientific neo-colonialism. In spite of peripheral states efforts, the possible development of these regions is an always faced and never overcome challenge. Transformations of the scientific-technologic system, which are more and more scientifically and technologically dependent every day, constitute as a gap. Key words: Science; technology; poverty; scientific-technologic gap
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".