Bibliographic record
Abstract
der Tuin 'On Research "Worthy of the Present"' Iris van der Tuin's provocative and intellectually generous cartography of 'research worthy of our time' spurs us to consider, or more to co-co-respond with the most pressing questions and problems of contemporary academia, those spilling out from the algorithmic condition (Van der Tuin, 2019.p. 9), the drain of hyper-individualism, the precarious but necessary project of decolonizing research.We co-co-respond with these ideas situated in a North American university officially committed to reconciliation with indigenous peoples (SFU Aboriginal Reconciliation Council, 2017), and equity, diversity and inclusion (Simon Fraser University, 2018).We are learning that these goals are fraught with contradictions and vulnerable to competing forces, to the overdetermined practices and discourses of competition, individualism and whiteness.Van der Tuin encourages us to attend to the differences that have been produced in our schools and other learning environments-differences of race, gender, language, ability, economic status-but make sure we use theories that do not continue to reify these differences so that they become further inscribed in our institutions.We must be wary of the rush to a universalist vision of reconciliation, diversity and unity that erase differences and complexities.In these entangled colonial and algorithmic pasts and presents our researcher lives too are implicated, we are respons-able for the agencies of our research materials, their lives, our lives in the world.Van der Tuin maps the potential to shift as we must from diversity to radical multiplicities, from indifference to difference.As Van der Tuin reminds us, our everyday academic life and labour are already deeply entangled in the coded objects of the algorithmic conditions, pulling us into new realms of academic capitalism.It is tempting in this hyper-individuated new world to hunker down and play it safe, or to play the metrics, compete, be visible, but perhaps not present, and to police our disciplinary (and physical) boundaries very carefully.After all, it is in the disciplinary space of education that we make connections, that we share stories, that we connect to teachers and
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 | 0.038 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".