Generational affinities and discourses of difference: a case study of highly skilled information technology workers<sup>1</sup>
Bibliographic record
Abstract
Sociologists theorizing the concept of 'generation' have traditionally looked to birth cohorts sharing major social upheavals such as war or decolonization to explain issues of generational solidarity and identity affiliation. More recently, theorists have drawn attention to the cultural elements where generations are thought to be formed through affinities with music or other types of popular culture during the 'coming of age' stage of life. In this paper, we ask whether developments in computer technology, which have both productive and cultural components, provide a basis for generational formation and identity and whether generational discourse is invoked to create cultures of difference in the workplace. Qualitative data from a sample of Information Technology workers show that these professionals mobilize 'generational' discourse and draw upon notions of 'generational affinity' with computing technology (e.g. the fact that people of different ages were immersed to varying degrees in different computing technologies) in explaining the youthful profile of IT workers and employees' differing levels of technological expertise.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".