Bibliographic record
Abstract
No matter how we archaeologists cloak ourselves in the protective garb of science, the reality is that archaeological interpretation is 80 percent random bursts of creative wonder and 20 percent evidentiary justification.In the introduction to the published version of Peter Pan, James Barrie proclaims that he cannot remember writing the play, that it conjured itself.I feel that Barrie's spirit must have whispered in my ear as I conducted this analysis, because once the random notion that Peter Pan was the story of early-twentieth-century gender relations flitted through my mind, I could see the archaeology of the Zeta Psi fraternity in no other way.In attempting to convince myself that using the play to interpret the site was an act of intellectual silliness, I only managed to see more and more levels of connection between the two.While Barrie may have claimed innocence in the authorship of his work, I will make no such claim.This was written neither in a dream nor without the help of many people.Just as interpretive inspiration comes from many sources, so does the will and energy to complete the writing task.I am unable to thank by name all my sources of inspiration and encouragement-and many may not even know of the role they played in creating this manuscript.Nameless members of lecture audiences, Zeta Psi alumni, friends, colleagues, students, family members, and archive and library staff, and even random strangers on cross-country flights, all provided inspiration, feedback, and support of various kinds.I will thank as many as I can, but also hope those not mentioned here by name will know their contributions to this project were invaluable to me.xiii
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.360 | 0.251 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".