Debilidades, Amenazas, Fuerzas y Oportunidades (DAFO) en las redes sociales
Bibliographic record
Abstract
As well as a first step in strategic planning within production realm, a SWOT analysis of a system-processproject in relationship with its environment is often carried out. Correspondingly, the different types of social networks can also be regarded as socio-technological environments used by social groups in the development of different projects; each of them with its own Strengths and Weaknesses in the use of such networks, which in turn entail generic and specific Opportunities and Threats to these groups. Furthermore, each social network, seen as a set of techniques and users, adopts forms of communication which enable/encourage the emergence, consolidation or disappearance of certain organizational models, for instance, with a different degree of horizontality, hierarchical, permeable or manipulated by the groups. These models are analyzed using organizational schemes, as particularly studied by Mintzberg who considers their design parameters and contingency factors. The paper deepens the analysis of deviations risks in the very complex systems-processes-projects which can be originated by the users of social networks – though with a high degree of uncertainty –, as well as their contribution feeding back the development of types and forms of the own social networks. Moreover, special attention is focused on synergies of different intensity in the acceleration of real changes within such social networks and their generated relationships, and particularly towards the likely creation of new systems of relations (in material and intellectual production, distribution, etc.) in all fields of economy, sociology, politics 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 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".