Bibliographic record
Abstract
INTRODUCTION When I published a book called Language, Society and Identity in 1985, the final word in the title was not a particularly common one in the social-scientific literature. There had, of course, existed all sorts of studies of ethnic and national affiliation – largely from political and historical perspectives – but it is only in the last few decades that studies of identity have really come into their own. Gleason (1983) argues that this emergence was fuelled in part by the writings of the neo-Freudian Erik Erikson (1968), and it is certainly the case that his writings in the 1950s and 1960s put identity development (and identity ‘crisis’) in the spotlight. More subtly, Erikson's work situated these individual phenomena in their social contexts. Besides that, he was a pioneer in what came to be known as psychohistory, with notable biographies of Luther, Gandhi and other important figures. Erikson's work thus provided a psychological addition to earlier studies of ‘groupness’, an addition that stressed identity in context. It is not surprising, then, that Gleason reports the gradual emergence of entries relating to identity in social science encyclopaedias of the 1960s – from what had been a virtually complete absence a generation earlier. As Joseph (2004) has noted, the early 1980s saw the appearance of important studies focusing on the linguistic aspects of identity. He mentions Gumperz's (1982) important collection on language and social identity, as well as Le Page and Tabouret-Keller's (1985) monograph on the subject.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".