Bibliographic record
Abstract
It seems certain to me that I will die and stay dead. By “I”, I mean me, Greg Nixon, this person, this self-identity. I am so intertwined with the chiasmus of lives, bodies, ecosystems, symbolic intersubjectivity, and life on this particular planet that I cannot imagine this identity continuing alone without them. However, one may survive one’s life by believing in universal awareness, perfection, and the peace that passes all understanding. Perhaps, we bring this back with us to the Source from which we began, changing it, enriching it. Once we have lived – if we don’t choose the eternal silence of oblivion by life denial, vanity, indifference, or simple weariness – the Source learns and we awaken within it. Awareness, consciousness, is universal – it comes with the territory – so maybe you will be one of the few prepared to become unexpectedly enlightened after the loss of body and self. You may discover your own apotheosis – something you always were, but after a lifetime of primate experience, now much more. Since you are of the Source and since you have changed from life experience and yet retained the dream of ultimate awakening, plus you have brought those chaotic emotions and memories back to the Source with you (though no longer yours), your life & memories will have mattered. Those who awaken beyond the death of self will have changed Reality.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 0.023 |
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".