The Logical Framework Model and the Theory of Change: Bases for the Strategic Planning of Innovation with Social Impact, in a Mexican Public Research Center
Bibliographic record
Abstract
Purpose. A conceptual proposal model based on the Logical Framework Model and the Theory of Change for the social impact strategic planning of innovation in the Public Research Centers (CPIs) of the National Council of Science and Technology (CONACYT), Mexico.Methodology. The study implied the context of the CPIs, with a bibliometric study of the Logical Framework and the Theory of Change in the second semester of 2022.Findings. As a theoretical contribution (Scientia), a conceptual proposal model, based on the Logical Framework Model and Theory of Change for the social impact strategic planning of innovation in the CPIs. As a practical contribution (Praxis), the conceptual proposal model relationship with the processes of a CPI that requiring clarity and speed for a highly changing environment.Originality. The research is valuable, original, and unprecedented for combining the Theoretical Framework and the Theory of Change that produce a social impact in a CPI.Conclusions and limitations.• Logical Framework Model and Theory of Change are possible to be included in CPI processes to achieve the social impact strategic planning of innovation.• The limitations are the knowledge and documentary interpretation of the processes of a CPI in social impact strategic planning of innovation.• Future studies propose to carry out a practical intervention that allows the validation of the study.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".