Bibliographic record
Abstract
Writing Prompt sent to the International Engineering, Social Justice, and Peace community and other engineering education sub-communitiess (primarily in North America: Our objective is to capture your thoughts, experiences, and responses to intersecting crises of COVID-19, white supremacy, anti-blackness, police violence, late capitalism, technologies and engineerings, power formations, state violence, academia, and engineering education over the past year. We wish to break the mould and create a space for the entire engineering community - students, educators, and professionals to share varied perspectives. Being oral history, this project is free from the usual academic barriers or gatekeeping. No citations needed if you do not wish to do so. While we aim to keep editorial interference at a minimum, we do not intend to include entries that (in our aesthetic and axiological judgement) can cause significant structural, cultural, or emotional harm to marginalised communities. We recognise that such filtering is hard to fully specify. The "objectives" statement above could be a guide for providing you a sense for what we are looking for. Entries should align with IJESJP's focus on engendering dialog on engineering practices that enhance gender, racial, class, and cultural equity and are democratic, non-oppressive, and non-violent. We acknowledge that even this filter limits the expression of particular forms of knowing and being. Our commitments are available here: http://esjp.org/about-esjp/our-commitments We are inspired by the way stories are told and archived through oral history, and feel the need to capture these stories before they become lost in the flux of our ongoing crises. Such history can be a story, anger and frustrations through rant, back of the envelope ideas and theories, poems, prose, fiction, critiques. This history is anything and everything you wish to document in time. Instructions: Please provide the following information by August 15th, 2021. Entry. Title, optional File upload, optional. Name, gender pronouns, and affiliations of authors Do you want your submission anonymous?
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 | 0.019 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.039 | 0.018 |
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".