Networking the field : fuzzy groups, fieldwork and the value of comparison
Bibliographic record
Abstract
Commencing research with a fuzzy definition of the population to be studied makes it possible to trace different perceptions of who respondents consider to be part of that population. While this can be extremely insightful and moves away from traditional notions of 'the group', it also poses a number of challenges for the researcher, both in the field and during data analysis. This paper focuses on a research project carried out in two geographically defined field sites, London and Toronto, but for which the relevant field was identified through participant observation and personal network interviews. The field is thus demarcated through social contacts and places of interaction but also through respondents' perceptions of the city. Focussing broadly on people who have moved to these cities from the South Pacific, with the aim of better understanding super-diversity in both cities, during fieldwork one main challenge has been identified: If 'the field' is not pre-assumed and!defining its parameters is integral to fieldwork itself, then how can unanticipated differences in the 'emergence' of the field in multiple field-sites be dealt with? This question fits squarely within the discussion on cross-national research. Relevant aspects of this literature will be reviewed, and drawing on the above-mentioned study it will be argued that there is a need to more critically assess the value of multi-site comparative research, not only for its potential to cross-verify findings but also for its scope to aid understanding of the complexities of the social settings being studied.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".