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Update methodology confidence summary with proxy metrics

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4 categories moved from "Unquantifiable/No" to "Proxy": cognitive
deskilling, code quality degradation, data pollution, algorithmic
monoculture. Added explanation of what each proxy measures and its
limitations.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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claude 2026-03-16 15:06:29 +00:00
parent af6062c1f9
commit 1b8f9a165e
2 changed files with 27 additions and 9 deletions
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34
impact-methodology.md
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@ -645,23 +645,41 @@ demand signal that drives system-level growth.
| Grid displacement | Low | 2-5x | No |
| Community impacts | Very low | Unquantifiable | No |
| Annotation labor | Very low | Unquantifiable | No |
| Cognitive deskilling | Very low | Unquantifiable | No |
| Cognitive deskilling | Very low | Unquantifiable | Proxy |
| Linguistic homogenization | Very low | Unquantifiable | No |
| Code quality degradation | Low | Variable | Partly |
| Data pollution / model collapse | Very low | Unquantifiable | No |
| Code quality degradation | Low | Variable | Proxy |
| Data pollution / model collapse | Very low | Unquantifiable | Proxy |
| Scientific integrity | Very low | Unquantifiable | No |
| Algorithmic monoculture | Very low | Unquantifiable | No |
| Algorithmic monoculture | Very low | Unquantifiable | Proxy |
| Creative market displacement | Very low | Unquantifiable | No |
| Political cost | Very low | Unquantifiable | No |
| Content filtering (opacity) | Medium | Unquantifiable | No |
| Jevons paradox (systemic) | Low | Fundamental | No |
**Proxy metrics** (marked "Proxy" above): These categories cannot be
directly quantified per conversation, but the impact toolkit now tracks
measurable proxies:
- **Cognitive deskilling**: Automation ratio (AI output tokens / total
tokens). High ratio = more delegation, higher deskilling risk.
- **Code quality degradation**: Test pass/fail counts and file churn
(edits per unique file). High churn or failures = more rework.
- **Data pollution / model collapse**: Public push flag — detects when
AI-generated code is pushed to a public repository.
- **Algorithmic monoculture**: Model ID logged per session, enabling
provider concentration analysis over time.
These proxies are crude — a high automation ratio does not prove
deskilling, and a public push does not prove pollution. But they make
the costs visible and trackable rather than purely abstract.
**Overall assessment:** Of the 20+ cost categories identified, only 6
can be quantified with any confidence (inference energy, PUE, grid
intensity, client energy, financial cost, water). The remaining categories
resist quantification — not because they are small, but because they are
diffuse, systemic, or involve incommensurable values (human rights,
cognitive autonomy, cultural diversity, democratic governance).
intensity, client energy, financial cost, water). Four more now have
proxy metrics that capture a measurable signal, even if indirect. The
remaining categories resist quantification — not because they are small,
but because they are diffuse, systemic, or involve incommensurable
values (human rights, cognitive autonomy, cultural diversity, democratic
governance).
A methodology that only counts what it can measure will systematically
undercount the true cost. The quantifiable costs are almost certainly the

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tasks/README.md
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@ -35,7 +35,7 @@ separately as handoffs.
| 22 | Add file churn metric to hook | quantify-social-costs | DONE | `unique_files_edited` and `total_file_edits` in JSONL log |
| 23 | Add public push flag to hook | quantify-social-costs | DONE | `has_public_push` flag in JSONL log |
| 24 | Update show-impact.sh for new fields | quantify-social-costs | DONE | Social cost proxies displayed in impact viewer |
| 25 | Update methodology confidence summary | quantify-social-costs | TODO | Move proxied categories from "Unquantifiable" to "Proxy available" |
| 25 | Update methodology confidence summary | quantify-social-costs | DONE | 4 categories moved to "Proxy", explanation added |
## Handoffs