Bibliographic record
Abstract
Recently, attention has been drawn toward an overlooked and nearly forgotten personality type: the ambivert. This paper presents a genealogy of the ambivert, locating the various contexts it traversed in order to highlight the ways in which these places and times have interacted and changed-ultimately elucidating our current situation. Proposed by Edmund S. Conklin in 1923, the ambivert only was meant for normal persons in between the introvert and extravert extremes. Although the ambivert could have been taken up by early personality psychologists who were transitioning from the study of the abnormal to the normal, it largely failed to gain traction. Whether among psychoanalysts, psychiatrists, or applied and personality psychologists, the ambivert was personality non grata. It was only within the context of Eysenck's integrative view of types and traits that the ambivert marginally persisted up to the present day and is now the focus of sales management and popular psychology.
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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 | 0.002 | 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 | 0.002 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".