Using a virtual platform for an asynchronous co-operative inquiry
Bibliographic record
Abstract
A participatory action research (PAR) study using co-operative inquiry methods was employed to examine the topic ‘spirituality and childbirth’. Co-operative inquiry (CI) reclaims the right of co-researchers to create knowledge from their own lived experience, an approach that works ‘with’ rather than ‘on’ people, valuing individual contributions. Traditionally, CI has been organised synchronously in the same physical location. However, with current events and need for greater global collaboration for divergent/convergent perspectives, an ‘asynchronous’ CI is important to consider. To date, the authors are not aware of any published/unpublished asynchronous co-operative inquiry research projects. This article describes how our inquiry group worked across global regions and time zones meeting online, via emails and discussion boards, and gathered data in an online repository. The outcomes of this inquiry are published elsewhere, here we discuss the novel methods used to support emergence of a modified CI. We offer insight into how our cycles of reflection and action matured and were possible and enhanced through virtual inquiry methods. While technology posed limitations, working asynchronously across time and space enabled rich and complex conversations. An asynchronous modified CI method allows a depth of inquiry to be achieved whilst retaining the purpose of CI.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".