Inverted Apprenticeship: How Senior Occupational Members Develop Practical Expertise and Preserve Their Position When New Technologies Arrive
Bibliographic record
Abstract
New technologies create a dilemma for senior members of occupations. Traditionally, practical expertise and position are considered correlates, yet when new technologies arrive, they may be knocked out of alignment. This means that senior members must develop new expertise lest their position be threatened. However, because position often signifies expertise, developing new practical expertise may be challenging. Indeed, senior members face strong pressures not to appear to nor actually devote time to comprehensive formal training as they are booked with complex problems using prior methods, they are responsible for the learning of junior members, and they have passed early career training windows. Through comparative ethnographic field studies of urological surgery and investment banking, we show that “inverted apprenticeships,” defined as configured struggle and restructured interactions with junior members that allow senior members to develop practical expertise with new technologies while maintaining their position, resolve this dilemma. We identify four pathways that senior experts took to structure these inverted apprenticeships, including seeking, stalling, leveraging, and confronting. We uncover the conditions of each pathway and trace their consequences. Although these pathways allowed senior members to enhance or preserve their position, they generated widely varying practical expertise with the new technology. Furthermore, the majority of these pathways undermined the learning of those most junior, who were supposed to be developing expertise through their interactions with seniors. Funding: This work was supported by the Strategic Management Society [Grant SRF-2015DP-0063] and the Social Science and Humanities Research Council of Canada [Grant 752-2014-0378].
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".