WORDS, WORDS, WORDS ON THE ROLE OF LANGUAGE IN CURRENT ELCIC DISCUSSIONS
Bibliographic record
Abstract
This essay was composed at the request of the National Church Council of the Evangelical Lutheran Church in Canada to assist the church in considering the matter of the blessing of same-sex relationships. Nestled among our family cookbooks is a volume entitled Foods that Harm, Foods that Heal. So, too, words, names, labels: some can hurt, some can heal. Or mislead and confuse. “Conservative”? “Liberal”? We likely have some idea of what we mean when we apply these labels to others or to ourselves. But your “conservative ” may be my “liberal, ” and vice-versa. In discussing divisive issues, it’s important to see how words, labels, and names function, and how they can heal or harm. Names – Now, and Then Corporations have millions invested in their names, logos, and slogans, part of the “branding ” of almost everything today (Klein, 2000). They may invent a character – Betty Crocker, the Glad Man – and that character is that company for the general public: friendly, smiling, helpful, or sitting around waiting for a Maytag service call. Corporations do not want to acquire “a bad name. ” Nor do we. Indeed, in our digital age we may worry that our name – or our government or bank numbers – may be stolen and along with it our legal and financial identity, our public “person. ” On the other hand, while we anxiously protect our identifying “numbers, ” we may at the same time object to being simply “a number” rather than a name – a person. For names are not simply breath and sound, or symbols in black and white on a page. Unless a name deliberately masks the true purpose of a group (an anti-environmental group masquerading under a “green ” name, for example), a name represents the essence, the very being, of what is named: • An illness: if we can name it, we can hope to treat it. • A thing: “Go to sleep, it’s just a shadow on the wall.”
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.053 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".