The Inbetweeny Collective: Reflexive Dialogues on the Liminality of Researchers' Lived Experiences
Bibliographic record
Abstract
In this collaborative autoethnography, we critically explore our lived experiences within a wider context of HCI research and practice. We reflect on the epistemological ways of knowing through ‘insider’ lived experience of, and ‘outsider’ knowledge of our research topic(s) via the concept of “liminal space” as a process ontology. To embrace liminality entails inhabiting the space ‘in-between’ these ways of knowing, suspended on a threshold of uncertainty and transformative growth. All authors identify as ‘inbetweenies’, because we are neither just ‘insiders’ nor ‘outsiders’, and we collect our respective stories to share. Drawing from these stories and our dialogues, we discuss how ways of knowing have historically been dichotomously categorized with their associated subjective or objective characterizations, resulting in power hierarchies and tensions. We propose that for an ‘inbetweeny’ researcher, thoughtful approaches to navigating this liminal space could potentially bridge persistent tensions in HCI research and practices toward personal and systemic transformation. Five areas of reflection are discussed, with proposed learnings that can be applied towards sustained practices for individuals and collectives at any stage of their journey and development.
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 | 0.054 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.081 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".