Loves Me or Loves Me Not? Passing through the Forest of Love Symbols and Unveiling Its Social Nature
Bibliographic record
Abstract
This article unveils how love, as a signified, can be constituted by the artificially constructed symbolic signs (“signifiers”) represented in our everyday life. Only when we regard love as a symbolic system and try to decipher its meanings can we understand how love is transmitted through sociomental patterns. This article attempts to provide examples from language, symbolic materials, the imprinted body, the code of temporality, and the spatial aspect to interpret the general elements that commonly form the forest of love symbols. Moreover, this article introduces cognitive sociology as a significant analytic approach to examining love. On the one hand, taking the “semantic square” proposed by Zerubavel, I articulate that when we want to understand the meanings of symbols, we usually have to embed them into their symbolic context. On the other hand, based on the distinction between marked and unmarked social categories proposed by Brekhus, I explain that more often than not, we can shed light on the marked love types even when we focus on love issues. Last, this article reminds us that the symbols of love are not fixed and constant but change according to the transformations of context.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".