RECONSIDERING THE USE OF POST-POSITIVIST PARADIGM IN SOCIAL SCIENCES: IS IT POSSIBLE?
Bibliographic record
Abstract
This paper investigates the evolving landscape of research methodologies in the social sciences, focusing on the renewed interest in post-positivism amid growing dissatisfaction with strictly quantitative approaches. The study traces the experiences of a junior social work researcher struggling with paradigm identification, ultimately finding alignment with the adaptable and inclusive nature of post-positivism. By highlighting post-positivism's shift from a rigid quest for absolute truths to an emphasis on interactive dialogue and continuous learning, this research enriches the methodological discourse. The examination of post-positivism’s nuanced ontological, epistemological, and axiological perspectives underscores the significance of reflexivity and critical engagement in research. The study advocates for integrating qualitative and quantitative methods within the post-positivist paradigm to address complex social issues more effectively. It emphasizes the pivotal role of junior researchers in contributing to paradigm discussions and advancing the field, particularly in the scholarship of teaching and learning. By revisiting the post-positivist paradigm, this research encourages graduate students and emerging scholars to critically examine and understand the epistemological foundations that shape knowledge production. This exploration not only enriches their research but also equips them with essential skills for engaging with varied perspectives and advancing scholarly discourse. Article visualizations:
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 | 0.173 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.161 |
| Scholarly communication | 0.034 | 0.036 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.002 | 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".