Exploring context-based experience sharing: benefits, risks of over-formalization, and possible mitigations

[PoX-Praxis]

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I am currently exploring the usefulness of context-based sharing of experiences and information.

My working hypothesis is that if both the person seeking information and the person providing it explicitly share their context, it becomes easier to judge under what background and conditions a given approach was formed—regardless of whether the outcome was successful or not.

In other words, the goal is not to evaluate results, but to make it possible to assess whether a given method or decision-making approach was reasonable within its original context, and whether it might be comparable to one’s own situation.

Previous discussion:
https://github.com/orgs/community/discussions/185797

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What seems to work

From recent discussions and reflections, the following points have emerged regarding context-based experience sharing:

• It can help surface implicit or hidden assumptions that are usually left unstated.
• It may support calibration of judgment when comparing one’s own situation with others’ experiences.
• At the same time, there is a clear risk that over-formalizing context can lead to:
  • overconfidence (“this context must be correct”), or
  • rigidity, making it difficult to update or reinterpret context over time.

This raises the question of how context can be used without turning it into a fixed or authoritative framework.

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Possible mitigations against over-formalization

I would like to propose two tentative approaches and ask for critique.

1. Treating context models as provisional, editable hypotheses

Instead of treating context as a set of established facts, what if we explicitly framed it as a temporary, revisable hypothesis?

Concretely:

• Context would be declared as a working assumption.
• Third parties could examine it from perspectives such as:
  • whether important contextual factors are missing,
  • whether the assumptions are overly narrow or biased,
  • whether the context actually enables meaningful judgment between options.

This might allow context to remain something that is continuously refined, rather than something that becomes fixed and unquestioned.

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2. Presenting context primarily through free-form text

Another idea is to avoid rigid schemas at the input stage and instead ask people to describe their context in free-form text.

For example, rather than asserting a finalized context, individuals would describe:

• what they are currently thinking,
• what situation they are in,
• what kind of state or outcome they are aiming for.

From there, tools such as AI could assist by extracting and organizing contextual elements (constraints, intentions, assumptions), without forcing the person to prematurely formalize them.

This approach may:

• allow context to be shared even when the individual’s understanding is still incomplete,
• preserve room for reinterpretation and later revision.

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Questions for discussion

• Does treating context as a hypothesis actually reduce rigidity, or does it introduce new forms of authority or social pressure?
• Where does the balance lie between useful structure and harmful over-formalization?
• Does free-form context input meaningfully improve judgment, or does it increase ambiguity and misinterpretation?
• Are there known patterns or counterexamples where similar approaches failed?

I am not proposing an implementation or product here.

I am specifically looking for structural critiques, failure modes, and reasons why this abstraction may or may not hold up as a shared concept.

[ashiqrun]

Subject: Thoughts on context as a hypothesis vs. rigid schemas

This is a fascinating abstraction. Regarding the specific questions you raised about mitigating over-formalization:

1. Context as a "Revisable Hypothesis" I believe framing context as a hypothesis (Approach Welcome to GitHub product feedback #1) significantly reduces rigidity and social pressure. It changes the social contract of the information sharing from "This is the absolute truth" to "This is my current model; does it fit your parameters?"

Why it works: It lowers the barrier for others to offer corrections. It is much easier to say "I think this variable in your hypothesis is different for me" than to argue against a stated "fact."

Risk: The only risk of authority comes if the "hypothesis" is presented using jargon that excludes others, but explicitly labeling it as "provisional" usually mitigates this.

2. Free-form Text vs. Ambiguity The approach of allowing free-form input and using AI to organize it (Approach Feature Request: Select a column when adding an issue to a project #2) is likely the most viable path forward for user adoption.

The "Friction" Problem: Humans are historically bad at maintaining rigid metadata or filling out complex schema forms (high friction). We are, however, excellent at telling stories (narratives).

The Solution: Let the user provide the "messy" narrative context, and use tooling (like LLMs) to extract the structured "hypothesis" from it. This prevents the "garbage in" problem where users just tick random boxes to get past a rigid form.

3. Failure Modes A common counterexample/failure mode in Knowledge Management systems is Staleness due to Maintenance Burden. If the "context model" is too complex, nobody updates it when the situation changes. The system must be low-friction enough that updating the context (the hypothesis) is as easy as realizing it was wrong.

[MasteraSnackin]

Treating context as explicit but provisional is powerful, but it brings its own design and social risks.

Building on the previous answer

I agree with the earlier reply that framing context as a hypothesis lowers social pressure and makes disagreement easier, because people can "tune" variables instead of attacking someone's story. Where I'd extend it is:

• Hypotheses need visible versioning: if context is "my current model," it helps to show when it was last updated and what changed, so others can judge staleness without reading everything.
• Hypotheses need multiple voices: if only the original author can edit the context, it can still become an authority; lightweight "annotations" or "counter-models" (e.g., "same scenario, but at a 10‑person startup") keep it plural rather than canonical.

So: treating context as a hypothesis does reduce rigidity, but only if the surrounding social/UX patterns keep it easy to fork, annotate, and retire hypotheses instead of letting one "model" silently become the default.

Structure vs. over‑formalization

Useful structure usually emerges if you:

• Keep capture informal, interpretation structured: let people write narrative, but let tools derive candidate structures that remain obviously machine‑generated and editable, not "the truth."
• Favor few, high‑leverage dimensions over rich taxonomies: three or four contextual axes that strongly affect transfer (e.g., team size, risk tolerance, time horizon, domain) often beat large schemas that nobody maintains.
• Make structure local, not universal: different domains will naturally evolve different "context templates"; forcing a single schema across all discussions is where over‑formalization and authority creep in.

A practical illustration: instead of a 20‑field context form, you might show a short, editable "summary chip" generated from the story (e.g., "2‑person OSS project, volunteer time, high experimentation tolerance"), and let the author fix or delete it.

Free text, ambiguity, and tooling

Free‑form input clearly improves participation and honesty, because people talk the way they think; they will not keep up with rigid metadata over time. But ambiguity is real, so the key is how you mediate between narrative and structure:

• Use tools to propose multiple readings of the same context ("Is this mainly about constraints, or about goals?") rather than one flattened interpretation.
• Always show the original narrative alongside any extracted structure, so readers can check whether the model oversimplifies or misframes the situation.
• Encourage responders to edit the extracted context, not just the text (e.g., "I'd classify this as medium risk, not low"), turning the context model itself into a conversational object.

In other words, free‑form input helps judgment when it is coupled with transparent, revisable structuring; it hurts when structuring is invisible or appears authoritative.

Failure modes and structural critiques

Some structural problems and failure modes you might want to consider explicitly:

• Context capture fatigue: even with hypotheses, if users feel they must always provide a "proper" context block, they may default to minimal or performative context ("team of 5, agile, etc.") that looks formal but is not actually informative.
• Context drift: if the situation evolves but the context snapshot doesn't, people may overtrust outdated models; versioning, timestamps, and prompts like "Has this changed since X?" help mitigate this.
• Context as gatekeeping: in some communities, "good context" can be used to police newcomers ("your context description isn't rigorous enough"), recreating the authority problem under a different name. Designing for "rough but honest" context is important here.
• False comparability: when two stories share similar structured context labels, people might over‑generalize ("it worked for them in 'small startup / high risk' so it should work for me") while ignoring subtle but crucial differences the schema doesn't capture.

A useful test is: "Can someone safely ignore the structured context and still follow the story?" If not, your structure may be doing too much invisible epistemic work.

Possible refinements to your abstraction

If you want to sharpen the concept as a shared pattern, you might:

• Name the pattern something like "narrative‑first, model‑second context sharing", to emphasize the priority ordering.
• Make "context hypothesis" explicitly multi‑party: initial author proposes, others can attach alternative hypotheses or "context deltas" without challenging the story itself.
• Treat staleness and multiplicity as first‑class: show "This context has 3 alternative hypotheses and was last touched N days ago," making uncertainty visible rather than pretending to a single, stable frame.

These tweaks keep your abstraction focused on its core strength: helping people reason about when and why an experience transfers, without turning context itself into a rigid artifact that must be obeyed.