Justice as inclusion: a critical conversation about inclusion and belonging
Bibliographic record
Abstract
Using Kovach’s conversational method and with our epistemological assumptions (knowing through embodiment, lived experience and scholarship) three leisure scholars used conversation and storytelling as method to explore current understandings of inclusion and belonging that perpetuate the violence of colonialism and the heteropatriarchy. For example, inclusion as it is often enacted is a token gesture of an organization but without making any of the structural changes necessary to ensure true belonging. Much like current rhetoric around decolonization, inclusion can become a metaphor that ultimately maintains the notion of settler futurity. Reframing inclusion to ‘justice as inclusion’ insists that practitioners and scholars, for example, de-program essentialist and capitalist notions of what we imagine Indigeneity to be, name systems of oppression and privilege, and centre Indigenous notions of relationality, including emphasizing the experience of connecting over what it means to be human, and establishing and re-establishing a connection to the non-human world.
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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.035 | 0.128 |
| Scholarly communication | 0.020 | 0.037 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".