The Rise of Neo-Experts: Sources, Characteristics, and Implications
Bibliographic record
Abstract
Experts are central to today’s knowledge economy. Yet despite their centrality, our understanding of their expertise remains surprisingly nebulous. This article offers more conceptual clarity around this question of expertise by detailing its varying sources, characteristics, and implications. The impetus for such a revisiting is the increasing lay intrusion, enabled by social media, in professional domains, and the parallel rise of what we label neo-experts. We identify four distinct kinds of actors that may claim expertise: traditional experts, lay-experts, non-experts, and neo-experts. By traditional experts, we mean people typically embodying legitimized expertise (such as lawyers). Lay experts (such as patient activists) are individuals who aspire to gain a substantive understanding of a domain and who want to convince legitimized experts to revise their views. They differ from non-experts or people who develop a limited understanding of a domain based on their unique experiences. Increasingly, however, we note the rise of neo-experts who operate in a yet to be claimed jurisdiction and self-develop a form of practice-based expertise that a following recognizes as such. By accounting for all these experts in the making of expertise, we provide a fuller account of expertise and discuss how modern technologies render it more inclusive.
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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".