Praxis for Accelerated Improvement in Research (PAIR)
Bibliographic record
Abstract
This article introduces the Praxis for Accelerated Improvement in Research (PAIR) as a transformative research management paradigm drawn from the participatory action research program focused on research production and publication in a private higher education institution in Manila, Philippines. PAIR mentoring scheme upholds establishing a committed and caring relationship between the mentor and the mentee, thereby developing a shared vision towards research. PAIR mentoring further underscores the need to institute a university research infrastructure to support its research programs and initiatives. This participatory and transformative approach to research management tendered significant (and accelerated) improvement in the Scopus® metrics of the university. Reflecting from the researchers’ and research participants’ journey in implementing and embracing change and improvement in the university research programs, this article argues that researchers need to advance connectedness, conviviality, optimism, shared vision, and prudence in all aspects of research. This article thereby recommends learning and researching within the lens of participatory and transformative paradigm. The authors further recommend to higher education institutions establishment of a sustained mentoring program where mentors and mentees mutually agree and commit to advance the research vision of the university collectively. Finally, this article reasons in favor of an institutional research infrastructure that nurtures not just the knowledge and skills in research, but also the attitude and values of its research stakeholders towards research and the overall research program of the organization.
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 | 0.240 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.005 | 0.041 |
| Research integrity | 0.007 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".