Towards understanding the human condition(s) of alone/not alone
Bibliographic record
Abstract
The difference between being alone and not alone is a matter of perception. To achieve a state of being not alone a person must overcome the stigma and fears attached to ideas of aloneness. Much of the ability to do so lies in that person's connections to their internal other(s), what could be described as a sense of self. These are the conclusion of a study of my own and others' experiences. This study was conducted autobiographically, although I did not start out to do an autobiographical study. I started out trying to make sense of responses to questionnaires from both high school and university students, and observations of the world and the people around me. I found that the only sense I could make of my topic was my own sense, and so I pursued a critical reflection on my own experiences of alone/not alone. This is a study of lived experience in which the focus is on understanding multiple meanings of alone/not alone. As I looked for meaning and depth of meaning within myself, I had to surmount layers of resistance, such as previous training to be an objective observer rather than part of the study, and a resistance from within myself as I looked deeper into my own feelings of alone/not alone. All of this contributed to my understanding of alone/not alone as I slowly, and with some difficulty, brought myself into the study, experiencing various states of alone/not alone as I did so.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".