Bibliographic record
Abstract
Paper presented at the ISTP Conference, Toronto 2007. Mate choice, courting, parental investment, attractiveness, and love are a few examples of human interactions in which evolutionary psychology has a keen interest. Theories like this shed light on human partner preferences and there is quite strong empirical support for that as well. Roughly the general version says that \n \nFor any member of the sexes, if the individual is interested in long-term mating, it will select a mate that is showing to be \n1.\table to invest in the relationship and in their offspring \n2.\twilling to invest in the relationship and in their offspring \n3.\table to physically protect self, partner and offspring \n4.\thaving good parenting skills \n5.\tcompatible with the self (has similar values, age, personality etc.) \n \nThese studies show how the preferences in partner selection resemble each other. Over many countries and cultures, within and between the sexes, within and between age groups, social economic classes etc. people all are, to certain extend alike in what they like and dislike. The interpretation of what is alike, however, is a matter of debate. That is, “resemblance” always is resemblance in the eye of the persons or organisms that compare. \n \nSo a preference for a certain body shape, for symmetry, for sharp male facial contours and soft contours in the female face, for a particular waist-hip ratio etc. is in the eye of the beholder. That is, in the eye of the organism involved, or at least in the eye of the sex of the organism involved, or at least in the eye of the members of a certain age, culture, historical period of the sex of the organism involved, or at least…etc. \n \nFrom another angle there are studies of how personal knowledge of the human body partially accounts for the experience of romantic love in humans. Helen Fisher’s work is an example. Focusing on the experiences of human beings concerning romantic love, she reports remarkable regularities in and resemblances of feelings between human beings from different countries, cultures, ages, the sexes and even, apparently, between non-human animals – primates, and some other mammals in particular. What is remarkable here is that they mostly report of either internal feelings or the bilateral meaningful behavior and expected or hoped for behavior of the one in love and the one loved.From yet one other angle, Antonio Damasio’s theory on emotions and feelings also seems to enhance evolutionary psychology’s claims. Damasio suggests that vision, hearing, touch, taste and smell result from nerve activation patterns that reflect states of the external world. Emotions, on the other hand, are nerve activation patterns that correspond to the state of the internal world. The experience of sexual attraction is activated but also recorded in nerve cell activation patterns obtained by the brain from neural and hormonal feedback, and is experienced as a body state.Briefly, we argue, the way we interpret or represent our feelings, depends on how feelings are stylized, articulated, and expressed in communities. From (socially) skilled members of the group we learn how to appropriately deal with affects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".