Psychological Essentialism in Selecting the 14th Dalai Lama: An Alternative Account
Bibliographic record
Abstract
common-sense psychological essentialism as applied to objects could explain the selection process for the 14 th Dalai Lama in Tibet. Specifically, they argued that Tibetan bureaucrats represent the Dalai Lama’s personal objects as imbued with his essence. Because of this, they claimed, Tibetan bureaucrats test whether candidates are the reincarnation of the Dalai Lama based on whether the candidates can correctly identify, from a number of items, the one that belonged to the 13 th Dalai Lama. In this article, we draw on knowledge of the Tibetan Buddhist selection process to argue that Bloom and Gelman have overlooked the important role that the continuity of personal memories plays in the process of selecting the Dalai Lama. Finally, we propose that assumptions about the continuity of qualities for persons, rather than objects, drive the selection procedures. In our view, Bloom and Gelman’s claim that continued memory does not play a role in the object-identification task because some of the items are identical is not warranted. First, though some of the objects may appear to be identical to ordinary folk, they are not assumed to be identical to the Dalai Lama, who is supposed to have intimate familiarity with those he owned and this is exactly the point of the task. Second, when candidates are asked to identify
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".