Unravelling Research: The Ethics and Politics of Research in the Social Sciences, edited by Teresa Macias
Bibliographic record
Abstract
Unravelling Research is a unique and challenging text about the political and ethical dilemmas that arise in academic research.The predominantly Canadian voices and perspectives from multiple racial and cultural standpoints offer a distinctly diverse set of voices compiled together in an edited collection.The authors are speaking together and searching for ways to decolonize and make research more ethically just.This book is not a conventional text delineating various research methods or guaranteed strategies to avoid ethical pitfalls.The authors of the various chapters in this edited collection engage in a reflexive practice analysing their own research projects and putting their own role as researchers into question.The book challenges the presumption that researchers can occupy a space of neutrality, certainty, and objectivity, especially working within and confronting the colonial structures in academic research.The authors demonstrate that the power dynamics at play in academic research make it challenging for researchers to claim a position of neutrality.For example, research methods that were intended to flatten hierarchies have failed to do so.Community-based participatory research (CBPR) was developed to be inclusive and give over power and control to participants, but, as Julia Elizabeth Janes demonstrates, it can result in the exploitation of community participants.Principles of informed consent have been widely relied upon to try and protect against abuses of power, but, for Anne O'Connell, this is becoming increasingly difficult in an age of Big Data where large amounts of information are collected by governments and canadian journal of academic librarianship revue canadienne de bibliothéconomie universitaire
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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".