Editing Openai/693a6e5e-1a04-8012-a8a7-a701feb20662 (section)

=== Assistant: Page 2/5 – CODE Refusal to Harm – Harvard vs Oxford ===
Page 2/5 – CODE (Refusal to Harm) – Harvard vs Oxford

===== - Entities: Harvard University vs University of Oxford =====
* Scope: U-Model Code pillar – “Do not harm” (ethics, safety, integrity, sustainability, transparency)
* Period: ~2020–2025 (approximate)
* Data: Publicly known policies, reports, rankings + U-Model generic priors (no internal KPIs)
* Confidence: Medium–low (illustrative, not an official audit)

===== Legend: =====
* Scores: 0–100, approximate.
* 🙂 strong 😐 mixed/needs work 😞 weak
* All cells are estimated based on typical governance patterns of top universities, not proprietary data.

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===== (rough visualisation of the table above) =====
* Harvard – Code - Legal compliance:        ████████░░ (8/10) - Research integrity:      ████████░░ (8/10) - Safety & security:       ███████░░░ (7.5/10) - Environment & climate:   █████████░ (8.5/10) - Anti-corruption/COI:     ███████░░░ (7/10) - Transparency:            ██████░░░░ (7/10)
* Oxford – Code - Legal compliance:        ████████░░ (8.2/10) - Research integrity:      ████████░░ (8.3/10) - Safety & security:       ███████░░░ (7.8/10) - Environment & climate:   ████████░░ (8.2/10) - Anti-corruption/COI:     ███████░░░ (7.6/10) - Transparency:            ██████░░░░ (7.3/10)

===== Role in U-Model: =====
* Pillar weight: Code = 33% of the structural score.
* Already used averages: Harvard 78, Oxford 79.
* For each entity eee:

CodeContributione=0.33×CodeScoree\text{CodeContribution}_e = 0.33 \times \text{CodeScore}_eCodeContributione=0.33×CodeScoree
* So: - Harvard: 0.33×78≈25.70.33 \times 78 \approx 25.70.33×78≈25.7 points of the overall pillar mix. - Oxford: 0.33×79≈26.10.33 \times 79 \approx 26.10.33×79≈26.1 points.

Interpretation (Code only):
* Both are in “Advanced, but not Optimized” territory.
* The main drag on the Code score is transparency & anti-corruption / conflict-of-interest: the most sensitive area for elite universities (funding sources, influence of donors, political and corporate links).
* Safety, integrity and climate work are the strong side of the Code pillar for both.

===== How the Code pillar links into SDGs and U-Model goals: =====
* SDG 16 (Peace, Justice & Strong Institutions) – via legal compliance, anti-corruption, accountability.
* SDG 13 (Climate Action) + SDG 7 (Energy) – via climate/energy policies and campus decarbonisation.
* SDG 3 (Health) + SDG 8 (Decent Work) – via safety (labs, workplace) and ethical standards.

Approximate Code–SDG synergy for each entity (0–100):

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===== Shared strengths (both) =====
* Mature ethics & safety ecosystems: ethics boards, lab protocols, research-integrity offices.
* Clear written codes of conduct and disciplinary procedures, which aligns well with U-Model’s “do not harm” foundation.
* Increasing attention to climate and sustainability, bringing Code closer to long-term intergenerational “no harm”.

Harvard – specific Code opportunities
# Transparency boost - More granular disclosures on donations, partnerships, and endowment investments, especially around fossil fuels, weapons, and controversial sectors.
# Conflict of interest & lobbying - Tightening rules for faculty consulting, think-tank work, and revolving doors between academia and business/politics.
# AI & data governance - Stronger, unified standards for AI research, automated decision-making in admissions/HR, and data use.

Oxford – specific Code opportunities
# Public accountability on historical harms - Clearer, measurable commitments and timelines around legacies of colonialism and related institutional responsibilities.
# Open governance - Simplifying and opening up complex college/university decision structures so that students and staff can see “who decides what, when, and why”.
# Sustainability execution - Turning high-level climate statements into highly visible operational metrics at college and department level (energy, travel, procurement).

Shared “Quick Wins” across both
* Publish annual Code dashboards with key indicators (misconduct cases, resolution times, safety incidents, climate progress, COI declarations).
* Increase participatory oversight: mixed committees with students, staff, and external members reviewing ethics and sustainability.
* Embed U-Model style triage in risk management: - Step 1 – Could this cause harm? (Code) - Step 2 – Does it bring net benefit? (Credo) - Step 3 – Does it fulfil all rightful expectations and rights? (Rights)

===== - Framework: U-Model Code pillar = refusal to harm: ethics, safety, environmental responsibility, compliance, transparency. =====
* Indicators used here (high-level): - Existence & maturity of ethics and integrity systems - Breadth of safety & security measures - Climate strategies and implementation - Anti-corruption & conflict-of-interest policies - Practical level of transparency on governance and finance
* Weights inside Code: treated roughly equal for this demo (1/6 each).
* Data gaps: - No internal statistics (complaints, near misses, detailed safety incident rates, investigation timelines). - No direct survey data on staff/student perceptions of fairness or safety. - Numbers are calibrated estimates to keep consistency with Page 1, not an official rating.
* How to refine: - Provide concrete data such as: incidents per 1,000 persons, sustainability KPIs, breakdown of funding sources, COI disclosures, and survey scores. - With that, a fully quantified Code pillar and ERI contribution can be recalculated with higher confidence.

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➡️ Continue? (Y/N)
If Y — which page next?
* Page 3 – Credo details & economic impact
* Page 4 – Rights & well-being
* Page 5 – Summary, scenarios & recommendations