…
)”. So we need to output something like:…
We need to ensure total words 450-500 inclusive. Let’s aim for about 470 words. We need to include the e-book promotion paragraph at the end. We must use facts from e-book: -100 for COI detection, actions 1-4, adjacent, exact, framework, checklist, logistical fitness max 30 points, methodological fitness max 30, topical resonance max 40, trigger new manuscript submission form, +10 per matched core argument theme, +10 for high past acceptance rate >66%, +15 for “Available” status. We need to incorporate these specifics. We must not use placeholders. Must be actionable content for professionals. We need to count words. Let’s draft then count. We’ll produce title line: “Title: The Core Engine: Designing Your Automated Peer Reviewer Matching System” Then newline, then HTML. We’ll need to count words in the entire output after “Title:”? Probably includes title line as part of word count? Usually they count the article content. Safer to count everything after “Title:” line? We’ll include title line in count? We’ll try to keep total around 470 words including title line? Safer to make content ~460 words and title line adds few words. Let’s draft content. We’ll write several paragraphs. We need headings: maybe h2, h3 with wp:heading. We’ll use:…
But they said plain HTML paragraphs and headings (e.g.,…
). So we can use similar for headings. Let’s outline: Title line. Then intro paragraph. Then heading: How the System Works Paragraph describing trigger and actions. Then heading: Building the Methodology Weighting Scale Paragraph describing Exact, Adjacent, General, weighting. Then heading: Scoring Logistical, Methodological, Topical Fitness Paragraph describing points. Then heading: Applying Filters and COI Check Paragraph describing -100 COI, checklist. Then heading: Generating the Reviewer List and Notification Paragraph describing Action 4. Then heading: Implementation Checklist Bullet list? But bullet list would be HTML- . That’s okay but they said plain HTML paragraphs and headings; but we can still include lists; but safer to keep paragraphs.
We could do a paragraph with semicolons.
Finally e-book promotion paragraph.
Now count words.
Let’s draft and then count.
I’ll write draft:
Title: The Core Engine: Designing Your Automated Peer Reviewer Matching System
Automating peer reviewer matching turns a tedious editorial task into a rapid, data‑driven process that improves fit and reduces bias. This post walks you through building the core engine using the steps outlined in the e‑book.
Trigger and Data Flow
The workflow starts when a new manuscript submission form is completed. Immediately, send the abstract to your AI analysis tool (Chapter 4) to extract structured themes, methods, and core arguments.
Action 1: AI Abstract Analysis
The AI returns a JSON‑like list of topical resonance terms, methodological tags, and core‑argument themes. Store this output as the query basis for reviewer matching.
Action 2: Database Query for Thematic Fit
Query your reviewer database (Airtable or Google Sheets via API) for profiles whose stored keywords match the extracted themes. Use an exact match filter for primary methodology and an adjacent filter for related methods.
Framework: Methodology Weighting Scale
Label each match as Exact (methodology identical), Adjacent (closely related, e.g., content analysis for discourse analysis), or General (same discipline, different methods). Assign weights: Exact = 30 points, Adjacent = 20 points, General = 10 points toward the methodological fitness score.
Action 3: Logistical and Topical Filters
Apply three scoring pillars. Logistical Fitness (max 30) awards +15 for “Available” status, +10 for past acceptance rate > 66%, and +5 for recent review activity (< 12 months). Methodological Fitness (max 30) uses the weighting scale above. Topical Resonance (max 40) gives +10 for each matched core‑argument theme, up to four themes.COI Check and Disqualification
Before scoring, run a conflict‑of‑interest screen. Any detected potential COI triggers an automatic –100 point penalty, removing the reviewer from consideration regardless of other scores.Action 4: Generate Ranked List and Notify Editor
Sum the three pillar scores, sort reviewers descending, and compose an email to you with a ranked list, score breakdown of each reviewer’s point totals, and a short summary of why they matched.Implementation Checklist
1. Set up AI abstract analysis endpoint (Chapter 4). 2. Export reviewer profiles to Airtable/Google Sheets with fields: methodology, keywords, availability, past acceptance rate, last review date, COI flags. 3. Build a script that triggers on form submission, calls the AI, queries the database, applies the –100 COI rule, calculates logistics/methodology/topical scores, and emails the ranked list. 4. Test with five sample manuscripts to tune weights and thresholds.For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Niche Academic Journal Editors (Humanities/Social Sciences): How to Automate Peer Reviewer Matching and Manuscript Gap Analysis.
Now we need to count words. Let’s count manually. I’ll copy the text and count words. Title line: “Title: The Core Engine: Designing Your Automated Peer Reviewer Matching System” Words: Title:(1) The(2) Core(3) Engine:(4) Designing(5) Your(6) Automated(7) Peer(8) Reviewer(9) Matching(10) System(11). So 11 words. Now each paragraph content. I’ll go paragraph by paragraph. Paragraph 1: “Automating peer reviewer matching turns a tedious editorial task into a rapid, data‑driven process that improves fit and reduces bias. This post walks you through building the core engine using the steps outlined in the e‑book.
” Count words: Automating(1) peer2 reviewer3 matching4 turns5 a6 tedious7 editorial8 task9 into10 a11 rapid,12 data‑driven13 process14 that15 improves16 fit17 and18 reduces19 bias.20 This21 post22 walks23 you24 through25 building26 the27 core28 engine29 using30 the31 steps32 outlined33 in34 the35 e‑book36. So 36 words. Paragraph 2 (heading line not counted? Heading is separate but we need to count words in heading too? Probably yes as part of content. We’ll count heading text.) Heading: “Trigger and Data Flow
” Words: Trigger1 and2 Data3 Flow4 => 4 words. Paragraph after heading: “The workflow starts when a new manuscript submission form is completed. Immediately, send the abstract to your AI analysis tool (Chapter 4) to extract structured themes, methods, and core arguments.
” Count: The1 workflow2 starts3 when4 a5 new6 manuscript7 submission8 form9 is10 completed.11 Immediately,12 send13 the14 abstract15 to16 your17 AI18 analysis19 tool20 (Chapter 4)21 to22 extract23 structured24 themes,25 methods,26 and27 core28 arguments29. So 29 words. Next heading: “Action 1: AI Abstract Analysis
” Words: Action1:1 AI2 Abstract3 Analysis4 => 4 words. Paragraph: “The AI returns a JSON‑like list of topical resonance terms, methodological tags, and core‑argument themes. Store this output as the query basis for reviewer matching.
” Count: The1 AI2 returns3 a4 JSON‑like5 list6 of7 topical8 resonance9 terms,10 methodological11 tags,12 and13 core‑argument14 themes.15 Store16 this17 output18 as19 the20 query21 basis22 for23 reviewer24 matching25. 25 words. Next heading: “Action 2: Database Query for Thematic Fit
” Words: Action1:2 Database3 Query4 for5 Thematic6 Fit7 => 7 words. Paragraph: “Query your reviewer database (Airtable or Google Sheets via API) for profiles whose stored keywords match the extracted themes. Use an exact match filter for primary methodology and an adjacent filter for related methods.
” Count: Query1 your2 reviewer3 database4 (Airtable5 or6 Google7 Sheets8 via9 API)10 for11 profiles12 whose13 stored14 keywords15 match16 the17 extracted18 themes.19 Use20 an21 exact22 match23 filter24 for25 primary26 methodology27 and28 an29 adjacent30 filter31 for32 related33 methods34. 34 words. Next heading: “Framework: Methodology Weighting Scale
” Words: Framework1:2 Methodology3 Weighting4 Scale5 => 5 words. Paragraph: “Label each match as Exact (methodology identical), Adjacent (closely related, e.g., content analysis for discourse analysis), or General (same discipline, different methods). Assign weights: Exact = 30 points, Adjacent = 20 points, General = 10 points toward the methodological fitness score.
” Count: Label1 each2 match3 as4 Exact5 (methodology6 identical),7 Adjacent8 (closely9 related,10 e.g.,11 content12 analysis13 for14 discourse15 analysis),16 or17 General18 (same19 discipline,20 different21 methods).22 Assign23 weights:24 Exact = 3025 points,26 Adjacent = 2027 points,28 General = 1029 points3