The Nature of Gender: Gender Identity in Persons who are Intersexed or Transgendered
Bibliographic record
Abstract
We usually take for granted a harmony between our gender identity and our biological sex. However, persons who are intersexed or transgendered often experience this relationship as ambiguous or completely contradictory. They wonder whether they are female or male, neither or both. Their struggle raises important questions for all of us about the nature of gender. To what extent is our gender identity innate? Embodied? Is ambiguity in gender identity a relatively rare deviation from a dichotomous norm, or a reflection of a broader multiplicity of genders? The existence and experience of persons who are intersexed or transgendered challenge Christians to reflect seriously on our theologies of gender, and their implications for our understanding of what it means to be male and female. They also call us to consider how we might, in Christian community, seek to minister with persons who are intersexed or transgendered.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; 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".