Bibliographic record
Abstract
On the Nature of Expertise in SoTLIn 2004, David Pace published a paper in American Historical Review making a compelling case for the growth of a Scholarship of Teaching and Learning (SoTL) in history.In his title, Pace made reference to "the amateurs in the operating room" to describe the gap between the expertise historians bring to their research and that which they bring to their teaching.It is, of course, a gap that is not restricted to history.The phrase "amateurs in the operating room" resonated with many of us back in 2004, and in many ways it still does.We like to think that the gap between disciplinary research expertise and what Lee Shulman (1986) called "pedagogical content knowledge" has narrowed somewhat in the last 12 years, especially with the proliferation of programs for graduate students and post-doctoral fellows to help them develop instructional skills and knowledge.But the gap still exists.And another gap has entered the discourse-the perceived gap between disciplinary research expertise and SoTL-based research expertise.Many SoTL practitioners, including authors found in the pages of Teaching & Learning Inquiry, started out conducting their inquiries in a discipline other than SoTL.They may have received intensive training for this research as they pursued advanced degrees.Now they conduct inquiries into teaching and learning.From whence springs the expertise to do so?This question is fundamental to SoTL's impact and credibility.To answer it, we must take a close look at the concept of expertise.For many, expertise is something that one attains through hard work and exposure to other experts.Perhaps that work involves the 10,000 trials advocated in Malcom Gladwell's (2008) book, Outliers.However it is attained, the assumption is that, once attained, this expertise is then employed to make and disseminate further discoveries.We might call this the "all-or-none law" of expertise.Expertise is viewed as a milestone after which everything about us is changed.We get our 10,000 trials t-shirt.From this view, expertise is akin to licensing.You have it for life, perhaps pending occasional re-tests to be sure you haven't lost it.Academics who have been approached by members of the media to offer an expert opinion have probably encountered this view of expertise.Reporters are often unimpressed by answers to questions that are prefaced by "Actually, I'm still learning about this."They want to know: Are you an expert or are you not?They hope you are, because it is much more compelling to say that "experts have concluded…" than "learners have concluded…."Even so, it is precisely the "all-or-none law" that makes us uncomfortable about the notion of expertise.It doesn't make sense to think of expertise as something fully attained and sustained.The whole idea of the academy is to push boundaries and grow in new directions.Yet it is proponents of the "all-or-none law" who will argue that SoTL research is conducted by non-experts-amateurs in a new operating theatre.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.008 | 0.024 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".