Negotiating a Way In: A Special Collection of Essays on Accessing Information and Socio-legal Research
Bibliographic record
Abstract
For most scholars, what we choose to research is largely determined by personal interest and a desire to produce new knowledge that will meaningfully contribute to the advancement of our chosen field of study. For those of us who do work in the areas of criminology and socio-legal studies, this pursuit of knowledge often requires that we gain access to public officials, state institutions, or government documents to collect necessary data. Regardless of whether we engage in quantitative or qualitative research, the findings we produce are highly dependent upon the information we are able to gather. Access therefore emerges as a key topic for researchers, raising important methodological concerns as well as broader political questions regarding the availability of information in liberal democracies. However, although gaining access to data is a crucial step in the research process, it is one that seems to garner little scholarly attention within criminological and socio-legal circles. Indeed, although there are a countless number of textbooks on the market (many of which are used every year as required reading in undergraduate and graduate research methods classes) that discuss the various ways researchers can go about collecting and analysing data, very little is said about the processes involved in negotiating entry with the gate-keepers of these data. Much of what is discussed in most methods text-books and classrooms is often limited to questions of ethics and the moral duties and obligations of researchers to their participants, rather than critical considerations related to the actual practice of gaining access.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".