Bibliographic record
Abstract
I will begin by examining the term serial itself and its importance in the notion of the serial killer. What does the term serial say? It draws on series - a term often used in reference to novels, films and, quite literally, the television series. Series carries notions of multiple segments that are all linked in some way. There is an ongoing nature to the series - when one segment ends, another begins. The series creates anticipation, anxiety of what is to come and a hope for closure. As "serial killer" is a relatively new term it becomes possible to trace its inception and examine what is being revealed in this naming process. I then go to illustrate how this term serial is what sets serial killing apart from other forms of multiple murder (such as mass murder, spree killing, terrorism and assassination). I will explore the features of the serial killer that appear to make it unique to the collective by developing the language of the accounts of understanding the serial killer: the profiling account, the study of numbers, facts and statistics as a way to apprehend and comprehend the serial killer; the logical extension of society account, where the serial killer is a reflection of society from concerns with the individual to celebrity and consumerism; and the serial killer account, where the serial killer explains his own motivations. In looking at these accounts it becomes possible to see the cliches that are used to discuss the serial killer and how these reveal thoughts and fears of the collective, rather than providing the insight on the serial killer that the accounts are seeking.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.070 |
| Scholarly communication | 0.015 | 0.037 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".