Applied Reflexivity for Knowledge, Innovation and Resilience- Experimental Research Findings
Bibliographic record
Abstract
There is an emerging confusion in scientific literature between reflexivity and self-reflectivity. Self-reflectivity is a self-awareness of oneself own conduct to self-regulate feelings, thinking and action to reproduce acceptable social norms such as professional and socially best practices as citizens. Rather, reflexivity emerging literature refers to a metaconscious critical intellectual process and experiential transforming feelings, thinking and action to create new knowledge, innovate new actions and even improve resilience capability. This research argues that knowing how to do reflexivity will it as a transformative force enabling human development. The research question is: How to apply Reflexibility for Knowledge Development, Innovation and Resilience and what are the benefits? To answer this question a 17 year-long (2005-2022) experimental phenomenological action research was conducted. Findings reveal how reflexivity can be applied to one’s life experience, but also to knowledge, beliefs and values reaching ideological and social norms critical awareness enabling knowledge development and innovating new actions as an ability to practice resilience. The discussion addresses the long-term benefits of practicing reflexivity applied on knowledge and innovation building empowerment and resilience capability.
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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 | 0.051 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".