Church Language at the Service of Gender Justice: The Example of the Lutheran Churches in the United States (ELCA) and Canada (ELCIC)
Bibliographic record
Abstract
This article is aimed at presenting a certain limited and at the same time significant area on which such cultural and political pressure is exerted, i.e. the Churches’ language policies. The issue of language policy, which is seen as a symbol of actions undertaken for the sake of gender equality in church life, will be presented based on the example of the Lutheran Churches. This is because it is Lutherans who, in recent years, have been the most committed to promoting equality between, and equal rights for, women and men (especially in North America) in social, economic and political life as well as within the Church’s structures. The article begins with a presentation of the issue of gender justice in modern mainstream Lutheranism. Then the role of church language for this trend in the Church is shown as a tool in the fight for gender justice and for eliminating inequalities as well as negative stereotypes. Next, the official recommendations of the largest Lutheran Churches in North America are presented concerning the purification of church language and containing guidelines on its proper, non-discriminatory use. Finally, an attempt is made at presenting the results of Lutheran language policy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.016 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".