Build a Source-Linked Research Reading List
A small, searchable table with a topic, review state and source for each record. You have not reproduced Hassan’s project, validated its reported numbers or evaluated scientific quality. Preserve the original references when sharing your own list.
Use caseTurn CSV, documents, or search results into reviewable data
Learn from Hassan’s research-classification example, then try a small, source-linked exercise.
Source · Hassan · @nutlope · X companion
2026-09-21 · Editorial update; no live model call
Problem
Section titled “Problem”You have titles, abstracts and notes scattered across saved links. The useful task is not to ask a model to read everything without limits. First create a small table whose records point back to their sources, then assign a manageable set of topics. This guide uses a fictional reading list, not the author’s original dataset.
Before you begin
Section titled “Before you begin”- Five to twenty abstracts or notes you are allowed to process.
- A title or summary column and a stable identifier.
Sources
Section titled “Sources”1. Keep a source-linked working copy
Section titled “1. Keep a source-linked working copy”Start with the included reading_list.csv. Replace it later with your own allowed material. Keep full-text files separate. A missing abstract should be marked as missing rather than silently completed by a model.
2. Write topics before running the classifier
Section titled “2. Write topics before running the classifier”Define a few useful categories with specific boundaries. Avoid topics so broad that every paper fits. Decide how to handle overlap; for this single-label exercise, choose the primary topic and leave ambiguous records for review.
3. Separate summary writing from classification
Section titled “3. Separate summary writing from classification”Jev is documented as a structured-decision model. If your workflow needs new prose summaries, treat that as a different step and record its source and cost separately. The bundled Studio demo only classifies the text already present in your file.
4. Try a small local pass
Section titled “4. Try a small local pass”Open the Content sample in Studio and choose summary. Run the demo and inspect the record that contains both AI and business terms. Change its label manually. The purpose is to practice a workflow, not reproduce the author’s reported benchmark.
5. Save an independent check set
Section titled “5. Save an independent check set”Select a few additional records and label them manually before adjusting your instructions. Keep those records separate from the examples you used to tune the categories. Compare disagreements against the source text; do not treat a plausible category as a verified reading of the paper.
Practice sample: download reading_list.csv for local use; not a live run here.
Check the result
Section titled “Check the result”A small, searchable table with a topic, review state and source for each record. You have not reproduced Hassan’s project, validated its reported numbers or evaluated scientific quality. Preserve the original references when sharing your own list.
- You can identify the input, its source, and the fields that leave your system.
- Original identities, failed rows, uncertain cases and human corrections remain visible.
- You distinguish an offline fixture, an author demo and a live evaluation you ran yourself.
Continue reading
Section titled “Continue reading”- CSV preflight tutorial: blanks, duplicate IDs and wrong columns
- Compare CSV classification results by ID: a worked example
Can this directly read a PDF?
Section titled “Can this directly read a PDF?”This exercise starts with text. PDF extraction needs separate validation.