Intercultural parallax: Comparative modeling, ethnic taxonomy, and the dynamic object
Bibliographic record
Abstract
Abstract Comparative modeling is necessary for semiotic inquiry. To better theorize such pursuits, a reflexive turn is in order: comparative modeling needs comparative modeling. In search of experientially grounded analogies better suited for understanding, validating, scrutinizing, and accounting for the situation of the semiotic inquirer, this paper applies insights from Peircean process semiotics and Göran Sonesson’s extended theory of cultural semiotics toward two ends: one theoretical, the other applied. First, I undertake a critical review of recent scholarly and creative works that attempt to adapt concepts of “parallax” as a source domain for comparative modeling activities. I do this in order to continue laying groundwork for a more complex, systematic theory of reflexive semiotic modeling in human inquiry, building on my earlier work. Second, I explore a specific case study of comparative intercultural modeling: namely, nationalist ethnic classification strategies in China and Vietnam. While many researchers have considered the onomastic and geopolitical dimensions of state-sanctioned ethnic categorization programs in these two countries, little has been done to unpack the powerful visual and narratological strategies employed by both; and little has been done to compare the intercultural categories these strategies serve to legitimize. The Vietnamese classification program is clearly modeled on its Chinese counterpart historically, but important categorical mismatches emerge between the two that indicate the presence of hidden diversity. Comparing the two systems also leads to a number of discoveries with implications for further developing the theory of cultural semiotics. Ultimately, the function or purpose of parallax modeling is shown to both comprehend and point beyond nascent intercultural and intracultural models toward more complex blends, by holding all such relations in a comparative frame, not as irreconcilable positions but as a more developed composite sign indicating the presence of yet more deeply buried dynamic objects to be searched out through further collateral experience.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".