Bibliographic record
Abstract
What a city, Adelaide. ‘The city’s asleep and the church is bare’, someone said to me once about Adelaide. I never quite knew where this quote came from; I did Google it and couldn’t find it! A fitting description, the warmth, the sleep in between churches, Adelaide. ‘What kind of Italian are you?’ What do you read in this sentence? Probably nothing, really. The meaning, as is often the case, is given by the context, and by the sender. This was a message I received in my inbox in July 2019 – I was in Montreal. I ignored it. ‘You must be the cop-lover traitor kind of type.’ A follow-up message, same person, in December 2019, months later. Why? Why would someone you don’t know send you unsolicited messages like this one? Someone you know only by their family’s surname; someone that you know that they know who you are and what you do for a living. I guess to some people these messages could sound provocative or just like a nuisance. For me, they were about recognition. He knows that I know who he is and he counts on that when sending a few words that are not just provocative, they carry a pinch of intimidation. This was the second time I got unwanted attention and ‘friendly’ warnings from someone allegedly close to ‘ndrangheta clans. Of all places, in Adelaide, a city where nothing much seems to happen. The first time I had this unpleasant experience, it was my first time in Adelaide. I couldn’t really believe then that someone would tell me to ‘start doing something else’ and that my surname ‘attracted attention, you know?’ and ‘What? You think they don’t know you are poking your nose into their affairs?’ I couldn’t really believe that was happening to me, I didn’t really know anything about anyone, I had just started my research! Ah, how the optics of what you seem to do and know count more than what you actually do and know!
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.186 | 0.041 |
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".