SCIENOPS / SEEDING THOUGHTS
Practical thinking on evidence, AI evaluation and the decisions that determine where research and development effort goes next.
01 / PREDICTION
A model can generate or prioritise a hypothesis. Its value depends on the decision it informs and the independent evidence needed to assess it.
Ask in the review: which result would change the proposed action?
02 / TRACEABILITY
A source link makes a claim inspectable. Review must still establish that the passage supports the same population, conditions and conclusion, with the material qualifications preserved.
Ask in the review: does the source support the claim as written?
03 / APPLICABILITY
Missing evidence may call for another experiment or source. An unsuitable method requires a different approach. Recording that distinction makes the next investment more specific.
Ask in the review: what exactly would the proposed work resolve?
04 / REAL-WORLD DATA
Population selection, treatment patterns, follow-up and differences between groups shape an observed result. More records can increase precision while leaving a biased comparison unresolved.
Ask in the review: which assumptions support this interpretation?
05 / PROGRAMME MEMORY
A paused or stopped investigation can still inform later work. Preserve the evidence, alternatives and conditions that drove the decision, including what would justify revisiting it.
Ask in the review: can a future team reconstruct why this choice was made?
06 / PRACTICAL VALUE
Output quality is only part of an evaluation. Assess source traceability, completeness, reviewer effort and rework, then determine how any released capacity would be used.
Ask in the review: which improvement changes the work or the next decision?
