Bibliographic record
Abstract
It is raining in Manchester. In the popular imagination it is always raining in Manchester, a gross misunderstanding which overlooks the great variety of Mancunian weather, from the damned fog which Grieg demanded Adolph Brodsky get rid of as a condition of his visiting, to the snow that this year brought a White Easter. Thus, in the otherwise dreary summer of 2012, it was good to find myself back in Montreal for the first time since the turn of the century, being reminded of the kind of real summer of which of late we have seen so little. Even the one thunderstorm was of a sufficiently Beethovenian scale to put the average local downpour to shame. We don't, of course, go to IAML conferences to enjoy good weather, marvellous loca tions, and great food—although nobody has ever denied the importance of all three. Not the least we go to take part in a huge family reunion where we meet old friends and make new ones. Almost all of the contributors to this issue of Fontes were unknown to me be fore I met them in Montreal, where several gave presentations in the two sessions orga nised by the Commission on and Training, but I'd like to think that they have now become friends as well as professional colleagues. They have certainly done me proud in contributing such a rich variety of articles and I need to place on record my gratitude to all who have given of their time and expertise to provide them. Their contributions also stand as testament to how wide a remit is implicit in the term Service and Training. Any attempt to draw a clear division between its two constituent parts is likely to reinforce the extent to which the two overlap. As Verletta Kern points out in her discussion of online tutorial provision at the University of Washington, providing a service as often as not carries its own implication for training. She finds an echo in Remi Castonguay's thesis that the attractions of social networking need to be tempered by ad vice on using them responsibly. In her article on the changing role of reference services, Kirstin Dougan also makes it clear that the complex nature of music materials ensures that providing a reference service perforce often involves a large instructional component. Some contributors have given an overview of music library training at the national level. Jurgen Diet outlines the situation in the German higher education system and Holling Smith-Borne discusses the training programmes that the MLA provides in the USA. Paul Guise, as a non-librarian, offers a novel insight into the implications for Canadian music librarianship of rethinking approaches to business education. I have at tempted in my own article, not so much to present the content of the public training courses offered by the UK & Ireland branch of IAML as to posit a rationale as to why they have become necessary and to extract from them core skills which any such courses should enshrine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".